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1
Kholifah, Desiana Nur, Hendri Mahmud Nawawi, and Indra Jiwana Thira. "IMAGE BACKGROUND PROCESSING FOR COMPARING ACCURACY VALUES OF OCR PERFORMANCE." Jurnal Pilar Nusa Mandiri 16, no. 1 (2020): 33–38. http://dx.doi.org/10.33480/pilar.v16i1.1076.
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Optical Character Recognition (OCR) is an application used to process digital text images into text. Many documents that have a background in the form of images in the visual context of the background image increase the security of documents that state authenticity, but the background image causes difficulties with OCR performance because it makes it difficult for OCR to recognize characters overwritten by background images. By removing background images can maximize OCR performance compared to document images that are still background. Using the thresholding method to eliminate background images and look for recall values, precision, and character recognition rates to determine the performance value of OCR that is used as the object of research. From eliminating the background image with thresholding, an increase in performance on the three types of OCR is used as the object of research.
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Sai, Dr M. S. Sesha. "Candidate Authentication using OCR Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29972.
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This paper explores the application of Optical Character Recognition (OCR) techniques for authenticating candidates based on identity documents. As advancements in technology continue to redefine various industries, the integration of OCR into candidate authentication processes offers a streamlined and efficient solution. The aim is to design a system which gets the image of the identity proof and the details are being retrieved using the character segmentation which is done by a feature extraction optical character recognition algorithm (OCR). The authentication process encompasses document validation and implementation of security measures to safeguard against fraud.By combining YOLO object detection, Optical Character Recognition (OCR) and fuzzy matching technique ,this approach aims to enhance the accuracy and reliability of candidate authentication. This underscores the importance of a comprehensive authentication framework, incorporating both technological and procedural elements, to establish a robust and secure candidate authentication process in diverse contexts. Keywords: ID Verification,Optical Character Recognition (OCR), Text Extraction, YOLO,Object Detection,Image Segmentation, EasyOCR , Fuzzy Matching.
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Fathurrahman, Haris Imam Karim, and Chin Li-Yi. "Character Translation on Plate Recognition with Intelligence Approaches." Buletin Ilmiah Sarjana Teknik Elektro 4, no. 3 (2023): 105–10. https://doi.org/10.12928/biste.v4i3.7161.
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In recent years, the number of automobiles in Indonesia has expanded. This rise has a knock-on impact on street crime. On this problem based, a preventative road safety prevention system is required. This research contribution is to develop an efficient algorithm for detecting vehicle license plates. This study's technique incorporates artificial intelligence technology with character translation. Yolov3 and Yolov4 are the artificial intelligence systems employed in this study. The detection of objects in the form of license plates is the result of this approach. In artificial intelligence, object detection results are utilized as input for image processing. The image processing method is used to translate characters. Optical Character Recognition (OCR) is used to decode the characters in the image precisely. The artificial intelligence data training resulted in a 76.53% and 89.55% mean average precision (mAP) level. Using OCR, the system is capable of character translation. These results give an opportunity to develop more complex image-processing applications.
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Mangesh, Sarak, S. S. Patil Prof., and Abhijit S. Mali Prof. "Image Text to Speech Conversion using Optical Character Recognition Technique in Raspberry PI." International Journal of Engineering and Management Research 14, no. 3 (2024): 78–84. https://doi.org/10.5281/zenodo.12697339.
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Optical Character Recognition (OCR) is a subset of artificial intelligence and is a subset of computer vision. Optical Character Recognition (OCR) is the use of Raspberry Pi to convert scanned bitmap images of handwritten or written text into audio performance. OCRs designed for a variety of world languages are now in use. In this method the context subtraction method based on the Gaussian mixture is used to recover the area of the moving object. For text content, the function of text localization and recognition is used. The text localization algorithm and the Tesract algorithm and edge pixel distributions based on the gradient properties of the stroke directions were used to automatically translate text areas from the object in the Ada enhancement model. In the translated text areas text characters are converted to binaries, which OCR software understands. For the blind, known text symbols are strongly pronounced. The potential of the algorithm for the proposed text location. The text file describes the character codes using the Raspberry system, which recognises the characters by using Tesract's and Python, and the audio output is heard in the recognition step.
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Iskandar, Rodzan, and Mezan El Khaeri Kesuma. "Designing a Real-Time-Based Optical Character Recognition to Detect ID Cards." International Journal of Electronics and Communications Systems 2, no. 1 (2022): 23–29. http://dx.doi.org/10.24042/ijecs.v2i1.13108.
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This research 0aims to design a Real-time ID card detection based on Optical Character Recognition (OCR). OCR detects and records information into CSV files using a camera. Hopefully, it can become one of the administrative solutions in Indonesia by using existing identity cards using OCR in real time. This research method was carried out independently in August 2021 using ID cards as objects. The tool involved was a 320x320 pixel webcam camera on an HP Intel Core i5 7th Gen notebook. The software used by Easy OCR was Pytorch-based. ID cards were detected using an algorithm by TensorFlow object detection with SSD MobileNet V2 FPNLite 320x320 as the pre-trained model of Tensorflow. The researchers collected ID card images using a webcam with various light conditions and orientations and label them using labeling. The researchers trained it with only 20 photos. After 3000 training steps, the researchers obtained about 0.17 loss and 0.95. Thus, the ID card detection tool using OCR runs well.
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Moharkar, Lalita, Sudhanshu Varun, Apurva Patil, and Abhishek Pal. "A scene perception system for visually impaired based on object detection and classification using CNN." ITM Web of Conferences 32 (2020): 03039. http://dx.doi.org/10.1051/itmconf/20203203039.
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In this paper we have developed a system for visually impaired people using OCR and machine learning. Optical Character Recognition is an automated data entry tool. To convert handwritten, typed or printed text into data that can be edited on a computer, OCR software is used. The paper documents are scanned on simple systems with an image scanner. Then, the OCR program looks at the image and compares letter shapes to stored letter images. OCR in English has evolved over the course of half a century to a point that we have established application that can seamlessly recognize English text. This may not be the case for Indian languages, as they are much more complex in structure and computation compared to English. Therefore, creating an OCR that can execute Indian languages as suitably as it does for English becomes a must. Devanagari is one of the Indian languages spoken by more than 70% of people in Maharashtra, so some attention should be given to studying ancient scripts and literature. The main goal is to develop a Devanagari character recognition system that can be implemented in the Devanagari script to recognize different characters, as well as some words.
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C S, Anu. "Extract and Organize Information in Images with AI using IBM Services." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 2031–35. http://dx.doi.org/10.22214/ijraset.2022.45670.
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Abstract: OCR is a short form of Optical character recognition or optical character reader. By the full form, we can understand it is something that can read content present in the image. Every image in the world contains any kind of object in it and some of them have characters that can be read by humans easily, programming a machine to read them can be called OCR. In machine learning, data mining is one of the major sections that cover the extraction of the data from the different platforms. OCR (Optical Character Recognition) is part of the data mining process that mainly deals with typed, handwritten, or printed documents. These documents hold the data mainly in the form of images. Extracting such data requires some optimised models which can detect and recognize the texts. Getting information from complex structured documents becomes difficult and hence they require some effective methodologies for information extraction. In this article, we will discuss OCR with IBM Watson Natural Language Understanding API, a deep learning-based tool for localizing and detecting the text in documents and images.
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Shetty, Ashik N. "A Unified Flask-Based Framework for Image Text Recognition, Multilingual Translation, and Text Summarization." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 4759–63. https://doi.org/10.22214/ijraset.2025.69051.
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This study presents a comprehensive review of OCR (optical character recognition), Translation, and Object Detection Research from a single image. With the fast advancement of deep learning, more powerful tools that can learn semantic, highlevel, and deeper features have been proposed to solve the issues that plague traditional systems. The rise of high-powered desktop computer has aided OCR reading technology by permitting the creation of more sophisticated recognition software that can read a range of common printed typefaces and handwritten texts. However, implementing an OCR that works in all feasible scenarios and produces extremely accurate results remains a difficult process. Object detection is also the difficult problem of detecting various items in photographs. Object identification using deep learning is a popular use of the technology, which is distinguished by its superior feature learning and representation capabilities when compared to standard object detection approaches. The major focus of this review paper is on text recognition, object detection, and translation from an image-based input application employing OCR and the YOLO technique.
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Uddin, Imran, Dzati A. Ramli, Abdullah Khan, et al. "Benchmark Pashto Handwritten Character Dataset and Pashto Object Character Recognition (OCR) Using Deep Neural Network with Rule Activation Function." Complexity 2021 (March 4, 2021): 1–16. http://dx.doi.org/10.1155/2021/6669672.
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In the area of machine learning, different techniques are used to train machines and perform different tasks like computer vision, data analysis, natural language processing, and speech recognition. Computer vision is one of the main branches where machine learning and deep learning techniques are being applied. Optical character recognition (OCR) is the ability of a machine to recognize the character of a language. Pashto is one of the most ancient and historical languages of the world, spoken in Afghanistan and Pakistan. OCR application has been developed for various cursive languages like Urdu, Chinese, and Japanese, but very little work is done for the recognition of the Pashto language. When it comes to handwritten character recognition, it becomes more difficult for OCR to recognize the characters as every handwritten character’s shape is influenced by the writer’s hand motion dynamics. The reason for the lack of research in Pashto handwritten character data as compared to other languages is because there is no benchmark dataset available for experimental purposes. This study focuses on the creation of such a dataset, and then for the evaluation purpose, a machine is trained to correctly recognize unseen Pashto handwritten characters. To achieve this objective, a dataset of 43000 images was created. Three Feed Forward Neural Network models with backpropagation algorithm using different Rectified Linear Unit (ReLU) layer configurations (Model 1 with 1-ReLU Layer, Model 2 with 2-ReLU layers, and Model 3 with 3-ReLU Layers) were trained and tested with this dataset. The simulation shows that Model 1 achieved accuracy up to 87.6% on unseen data while Model 2 achieved an accuracy of 81.60% and 3% accuracy, respectively. Similarly, loss (cross-entropy) was the lowest for Model 1 with 0.15 and 3.17 for training and testing, followed by Model 2 with 0.7 and 4.2 for training and testing, while Model 3 was the last with loss values of 6.4 and 3.69. The precision, recall, and f-measure values of Model 1 were better than those of both Model 2 and Model 3. Based on results, Model 1 (with 1 ReLU activation layer) is found to be the most efficient as compared to the other two models in terms of accuracy to recognize Pashto handwritten characters.
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Monteiro, Gabriella, Leonardo Camelo, Gustavo Aquino, et al. "A Comprehensive Framework for Industrial Sticker Information Recognition Using Advanced OCR and Object Detection Techniques." Applied Sciences 13, no. 12 (2023): 7320. http://dx.doi.org/10.3390/app13127320.
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Recent advancements in Artificial Intelligence (AI), deep learning (DL), and computer vision have revolutionized various industrial processes through image classification and object detection. State-of-the-art Optical Character Recognition (OCR) and object detection (OD) technologies, such as YOLO and PaddleOCR, have emerged as powerful solutions for addressing challenges in recognizing textual and non-textual information on printed stickers. However, a well-established framework integrating these cutting-edge technologies for industrial applications still needs to be discovered. In this paper, we propose an innovative framework that combines advanced OCR and OD techniques to automate visual inspection processes in an industrial context. Our primary contribution is a comprehensive framework adept at detecting and recognizing textual and non-textual information on printed stickers within a company, harnessing the latest AI tools and technologies for sticker information recognition. Our experiments reveal an overall macro accuracy of 0.88 for sticker OCR across three distinct patterns. Furthermore, the proposed system goes beyond traditional Printed Character Recognition (PCR) by extracting supplementary information, such as barcodes and QR codes present in the image, significantly streamlining industrial workflows and minimizing manual labor demands.
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Gyu Jung, Yong, and Hee Wan Kim. "Design and implementation of lightweight vehicle license plate recognition module utilizing open CV and Tesseract OCR library." International Journal of Engineering & Technology 7, no. 3.3 (2018): 350. http://dx.doi.org/10.14419/ijet.v7i2.33.14184.
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Background/Objectives: In order to recognize the license plates automatically, we design and implement a vehicle license plate recognition module that extracts characters of license plate area using open source OpenCV and Terreract OCR library.Methods/Statistical analysis: The static image was binarized using OpenCV 's banalization function. After binarizing the image by adjusting the pixel values between adjacent pixels, the candidate region judged a license plate was derived. The final candidate was derived according to the proposed algorithm in the candidate region. The extracted plate area was analyzed by using the Tesseract OCR library, and characters were extracted as a character string.Findings: The vehicle license plate recognition module relates to character recognition in the field of computer vision. In this paper, we designed and implemented a module that recognizes a license plate by using open source, applying a proposed algorithm to a moving object as a static image. The proposed module is a relatively lightweight software module and can be used in other applications. It is possible to install the camera at the entrance of the apartment and can read the license plate to identify whether it is a resident or not. When speeding and traffic violations occur on the highway, the vehicle numbers can be automatically stored and managed in the database. In addition, there is an advantage that it can be applied to various character recognition applications through modification of a slight algorithm in the module.Improvements/Applications: In addition to character recognition, the OpenCV library can be applied to various fields such as pattern recognition, object tracking, and motion recognition. Therefore, we will be able to create technologies corresponding to various services that are becoming automated and unmanned.
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Samaga, Abhinav, Allen Joel Lobo, Azra Nasreen, et al. "Enhancing automatic license plate recognition in Indian scenarios." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 365. http://dx.doi.org/10.11591/ijece.v15i1.pp365-373.
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Automatic license plate recognition (ALPR) technology has gained widespread use in many countries, including India. With the explosion in the number of vehicles plying over the roads in the past few years, automating the process of documenting vehicle license plates for use by law enforcement agencies and traffic management authorities has great significance. There have been various advancements in the object detection, object tracking, and optical character recognition domain but integrated pipelines for ALPR in Indian scenarios are a rare occurrence. This paper proposes an architecture that can track vehicles across multiple frames, detect number plates and perform optical character recognition (OCR) on them. A dataset consisting of Indian vehicles for the detection of oblique license plates is collected and a framework to increase the accuracy of OCR using the data across multiple frames is proposed. The proposed system can record license plate readings of vehicles averaging 527.99 and 2157.09 ms per frame using graphics processing unit (GPU) and central processing unit (CPU) respectively.
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Reezky Illmawati and Hustinawati. "YOLO V5 for Vehicle Plate Detection in DKI Jakarta." Jurnal Ilmu Komputer dan Agri-Informatika 10, no. 1 (2023): 32–43. http://dx.doi.org/10.29244/jika.10.1.32-43.
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The odd-even rule on vehicle number plates in DKI Jakarta aims to reduce congestion that occurs in DKI Jakarta. The application of these regulations is constrained by the limitations of the manual supervision function by officers. This problem can be overcome by implementing intelligence in the form of detecting number plate objects with the YOLO v5 algorithm and the character extraction process with Optical Character Recognition technology using Tesseract OCR. Object detection technology will detect objects in the form of vehicle plates. The OCR method can extract the characters on the number plate, the extraction results can be processed into parameter categorization so that the program can distinguish between vehicles that violate the rules and do not violate the rules automatically and more effectively and minimize errors. Based on this research, the average percentage of objects detected in each video is 92.38%, and the average confidence value obtained in object detection is between 75.55%. The success rate of the character extraction process on number plates is 95.45%, and the average proportion according to the detected number plate category is 97.2%. The implementation of the YOLO Algorithm has succeeded in detecting license plates with odd and even categories on videos that can provide signs and save violations of vehicles that violate the odd and even rules.
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Gupta, Deepshikha. "License Plate Recognition: A Brief Overview." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 953–57. http://dx.doi.org/10.22214/ijraset.2023.56644.
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Abstract: The License Plate Recognition system is a technological solution designed to automatically capture and interpret license plate information from vehicles. Utilizing a blend of object detection, character segmentation, optical character recognition (OCR) and image processing, ANPR systems play a pivotal role in bolstering security, optimizing traffic management, and supporting diverse applications such as toll collection and parking management. Equipped with highresolution cameras and advanced algorithms, these systems are integral to modern surveillance and transportation, providing solutions that enhance safety and operational efficiency.
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Bhashith, Thaseen, Adamya H R, Akash S H, Dhanyashree B, and Disha D. "Smart Vision Assistant Glasses for Visually Impaired Persons." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem39932.
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In recent years, advancements in computer vision and natural language processing (NLP) have led to the development of highly accessible and assistive technologies. This technology leverages these advancements to create a system that provides real-time object detection and text recognition capabilities, integrated with speech synthesis for audio feedback. The system employs the YOLO (You Only Look Once) algorithm for fast and accurate object detection and an Optical Character Recognition (OCR) module for extracting text from captured images. Text-to-speech (TTS) technology is incorporated to deliver audio outputs, ensuring accessibility for users, especially those with visual impairments. This decentralized system operates on user commands and does not rely on cloud processing, ensuring faster response times and data privacy. By combining computer vision and NLP, this paper offers a cost-effective and portable solution for real-time assistive applications, empowering users to interact effectively with their surroundings through visual data processing and auditory feedback. Key Words: Computer Vision, YOLOv8, OCR (Optical Character Recognition),
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Wndu Gata, Dwiza Riana, Muhammad Haris, Maria Irmina Prasetiyowati, and Dika Putri Metalica. "Automated Indonesian Plate Recognition: YOLOv8 Detection and TensorFlow-CNN Character Classification." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 9, no. 3 (2025): 474–83. https://doi.org/10.29207/resti.v9i3.6310.
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The precise identification and reading of Indonesian vehicle number plates are important in many areas, including the enforcement of law, collection of charges, management of parking areas, and safety measures. This study integrates the implementation of the YOLOv8 object detection algorithm with three OCR methods: EasyOCR, TesseractOCR, and TensorFlow. YOLOv8 is capable of identifying license plates from images and videos at a high speed and reliability under different conditions and therefore is used in this study to perform plate detection in images and videos. After licenses are detected, OCR techniques are performed to segment and read the letters. Both EasyOCR and TesseractOCR performed moderately well on static images achieving accuracy rates of 70% and 68% respectively, but both suffered significantly lower performance in video scenarios. Of the 100 video frames, EasyOCR was able to correctly identify characters in 61 frames and TesseractOCR in 58 frames, while the TensorFlow-based model outperformed the other two with 75 correct recognitions. Furthermore, easy OCR and static images as input while the TensorFlow-based models completed them with 100% accuracy. This observation can be explained by its design, which utilizes a CNN with ReLU activation and Softmax outputs, trained on 10,261 annotated characters and was enhanced with five different data augmentation techniques. The model shows strong performance in its ability to handle dynamic conditions such as motion blur, changing light conditions, and rotation of the plate angle. The results underscore the drawbacks of one-size-fits-all OCR applications in real-world use cases and stress the need for bespoke model training, as well as hierarchical contouring, in the context of automatic license plate recognition (ALPR). This study provides additional insights into ALPR systems by delivering a robust, scalable, and real-time tool for plate and character recognition, which is essential for intelligent transportation systems.
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Oudah, Nabeel, Maher Faik Esmaile, and Estabraq Abdulredaa. "Optical Character Recognition Using Active Contour Segmentation." Journal of Engineering 24, no. 1 (2018): 146–58. http://dx.doi.org/10.31026/j.eng.2018.01.10.
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Document analysis of images snapped by camera is a growing challenge. These photos are often poor-quality compound images, composed of various objects and text; this makes automatic analysis complicated. OCR is one of the image processing techniques which is used to perform automatic identification of texts. Existing image processing techniques need to manage many parameters in order to clearly recognize the text in such pictures. Segmentation is regarded one of these essential parameters. This paper discusses the accuracy of segmentation process and its effect over the recognition process. According to the proposed method, the images were firstly filtered using the wiener filter then the active contour algorithm could be applied in the segmentation process. The Tesseract OCR Engine was selected in order to evaluate the performance and identification accuracy of the proposed method. The results showed that a more accurate segmentation process shall lead to a more accurate recognition results. The rate of recognition accuracy was 0.95 for the proposed algorithm compared with 0.85 for the Tesseract OCR Engine. 
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Anjali, Sawarkar Ankit Singh Arhat Notey Monika Dhole Shubham Shinde Prof. Aditya Turankar. "Detection of Medicine Information with Optical Character Recognition Using Android." International Journal of Research in Computer & Information Technology 7, no. 2 (2022): 35–38. https://doi.org/10.5281/zenodo.6676155.
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Generally, people don’t understand medical terms but need medicines, in this situation OCR is helpful and solves the problem. Healthcare Management is one of the most vital and fastest expanding sectors in the world today. People demand more medicine to deal with stress and other illnesses as lifestyles change. As a result, a huge amount of money is spent on medicines. The resulting medical waste is also enormous. According to the World Health Organization (WHO), the global medical waste rate (HCWGR) is 2.5 kg/bed/day. In India, this rate is 1.55 kg / bed / day. Most of this waste is the medicine we throw away because we don't have any data about them. This medical waste is growing day by day, endangering the planet. With Covid19, the economic situation is more serious than ever. People need to search for paths to save money to improve their financial situation. Our project describes the same issue. We developed an application that identifies medicines using Optical Character Recognition (OCR) in this project. The application will include information such as drug names, available illnesses, side effects, generics, prices, and simple tips.
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Sharma, Niresh, and Varsha Namdeo. "An Efficient and Robust Multi Directional Deep Learning Based Licence Plate Recognition." International Journal of Membrane Science and Technology 10, no. 2 (2023): 2151–63. http://dx.doi.org/10.15379/ijmst.v10i2.2783.
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Smart cities must have all the important characteristics to achieve their intended goals. Proper traffic management and controlling, increased surveillance and safety, and enhanced management and avoidance of incidents must be the priorities of smart cities. Meanwhile, license plate recognition (LPR) has become the most debatable topic in the research community due to various real-time applications, such as “law enforcement, toll-free processing, access control, and traffic surveillance.” Automated LPR is a technique based on computer vision to recognize vehicles with their number plates. This study discusses various “Deep Learning based LPR” techniques to detect and identify “alphanumeric characters” in number plate. The projected model works on “license based detection” and “character recognition.” This technique uses “Optical Character Recognition (OCR)” technology to detect and extract the alphanumerical numbers from the license plate. This study is based on secondary data collected from various studies conducted on Licence Plate Recognition using various Deep Learning models published in databases like Google Scholar, Science Direct, NCBI, etc. Deep learning has been used widely in applications related to computer vision in recent years with great perfection. It is a great solution for modern and traditional image processing, object detection, and feature extraction issues. It has been widely used in different stages of LPR like character segmentation, license plate recognition, and OCR.
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Pyae, Phyo Thu, Mie Tin Mie, Phyu Win Ei, and Thet Mon Cho. "Reconstructing the Path of the Object based on Time and Date OCR in Surveillance System." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2610–12. https://doi.org/10.5281/zenodo.3591773.
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The inclusion of time based queries in video indexing application is enables by the recognition of time and date stamps in CCTV video. In this paper, we propose the system for reconstructing the path of the object in surveillance cameras based on time and date optical character recognition system. Since there is no boundary in region for time and date, Discrete Cosine Transform DCT method is applied in order to locate the region area. After the region for time and date is located, it is segmented and then features for the symbols of the time and date are extracted. Back propagation neural network is used for recognition of the features and then stores the result in the database. By using the resulted database, the system reconstructs the path for the object based on time. The proposed system will be implemented in MATLAB. Pyae Phyo Thu | Mie Mie Tin | Ei Phyu Win | Cho Thet Mon "Reconstructing the Path of the Object based on Time and Date OCR in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27981.pdf
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Koponen, Jarmo, Keijo Haataja, and Pekka Toivanen. "A novel deep learning method for recognizing texts printed with multiple different printing methods." F1000Research 12 (April 20, 2023): 427. http://dx.doi.org/10.12688/f1000research.131775.1.
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Background: Text recognition of cardboard pharmaceutical packages with machine vision is a challenging task due to the different curvatures of packaging surfaces and different printing methods. Methods: In this research, a novel deep learning method based on regions with convolutional neural networks (R-CNN) for recognizing binarized expiration dates and batch codes printed using different printing methods is proposed. The novel method recognizes the characters in the images without the need to extract handcrafted features. In detail, this approach performs text recognition considering the whole image as an input extracting and learning salient character features straight from packaging surface images. Results: The expiration date and manufacturing batch codes of a real-life pharmaceutical packaging image set are recognized with 91.1% precision with a novel deep learning-based model, while Tesseract OCR text recognition performance with the same image set is 38.3%. The novel model outperformed Tesseract OCR also in tests evaluating recall, accuracy, and F-Measure performance. Furthermore, the novel model was evaluated in terms of multi-object recognition accuracy and the number of unrecognized characters, in order to achieve performance values comparable to existing multi-object recognition methods. Conclusions: The results of this study reveal that the novel deep learning method outperforms the well-established optical character recognition method in recognizing texts printed using different printing methods. The novel method presented in the study recognizes texts printed with different printing methods with high precision. The novel deep learning method is suitable for recognizing texts printed on curved surfaces with proper preprocessing. The problem investigated in the study differs from previous research in the field, focusing on the recognition of texts printed with different printing methods. The research thus fills a gap in text recognition that existed in the research of the field. Furthermore, the study presents new ideas that will be utilized in our future research.
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Samaga, Abhinav, Allen Joel Lobo, Allen Joel Lobo, et al. "Enhancing automatic license plate recognition in Indian scenarios." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 365–73. https://doi.org/10.11591/ijece.v15i1.pp365-373.
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Automatic license plate recognition (ALPR) technology has gainedwidespread use in many countries, including India. With the explosion in thenumber of vehicles plying over the roads in the past few years, automatingthe process of documenting vehicle license plates for use by lawenforcement agencies and traffic management authorities has greatsignificance. There have been various advancements in the object detection,object tracking, and optical character recognition domain but integratedpipelines for ALPR in Indian scenarios are a rare occurrence. This paperproposes an architecture that can track vehicles across multiple frames,detect number plates and perform optical character recognition (OCR) onthem. A dataset consisting of Indian vehicles for the detection of obliquelicense plates is collected and a framework to increase the accuracy of OCRusing the data across multiple frames is proposed. The proposed system canrecord license plate readings of vehicles averaging 527.99 and 2157.09 msper frame using graphics processing unit (GPU) and central processing unit(CPU) respectively.
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Cho, Hangseo, and Jongpil Jeong. "Realtime Detection of Table Objects and Text Areas for OCR Preprocessing." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 20 (June 21, 2023): 197–205. http://dx.doi.org/10.37394/23209.2023.20.23.
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OCR (Optical Character Recognition) is a technology that automatically detects, recognizes, and digitally converts text into images. OCR has a variety of uses, including reducing human error when viewing and typing documents and helping people work more efficiently with documents. It can increase efficiency and save money by eliminating the need to manually type text, especially when scanning documents or digitizing images. OCR is divided into text object detection and text recognition in an image, and preprocessing techniques are used during the original document imaging process to increase the accuracy of OCR results. There are various preprocessing techniques. They are generally classified into image enhancement, binarization techniques, text alignment and correction, and segmentation techniques. In this paper, we propose a special-purpose preprocessing technique and application called Table Area Detection. Recently, table detection using deep learning has been actively researched, and the research results are helping to improve the performance of table recognition technology. Table detection will become an important preprocessing technology for text extraction and analysis in various documents, and it requires a lot of research and accuracy. While many previous studies have focused on improving the accuracy of OCR algorithms through various techniques, this study proposes a method to discover and exclude false positives by introducing a factor called Table Area Detection.
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Edi Junaedi, Syabina Nur Pajriyanti, Muhammad Subali, and Adrian Maulama Ramadhan. "DIGITAL BUSINESS CARD (DiNa) APPLICATION USING CNN ALGORITHM AND OCR TECHNOLOGY AS A FORMAL INTRODUCTION SUGGESTION." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 601–9. https://doi.org/10.52436/1.jutif.2024.5.4.2250.
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The rapid development of technology makes digital business cards increasingly the first choice as a formal introduction tool that is more environmentally friendly, reducing dependence on the use of paper and ink. In addition to serving as a formal means of introduction, digital business cards are also an effective medium for conveying crucial information about an individual or company. The implementation phase of this application involves the utilization of Optical Character Recognition (OCR) as the main feature, with image pre-processing as a key step to improve reading accuracy, including noise reduction, data normalization, and compression. The process of optical scanning and location segmentation is the main foundation in processing data from Business Card images. The next step includes feature representation and extraction using TensorFlow's OCR technique to process the data efficiently. The integration of the OCR model into the API allows Kotlin-based mobile applications to communicate directly with the OCR model, providing real-time character recognition. The first trial aims to evaluate the accuracy and time taken by the OCR feature in recognizing each text on the Business Card. Tensorflow and Easy-OCR models with 41.86% accuracy were used for object detection and optical character recognition, resulting in a system that is efficient, responsive, and allows model updates without interrupting the main functionality of the application. The app successfully combines eco-friendly aspects with advanced technology, creating a modern solution to meet the needs of effective formal introductions. Thus, this Digital Business Card application is not only an eco-friendly alternative, but also realizes efficiency in retrieving identification information directly through a mobile platform.
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Khomenko, Tat'iana Vladimirovna, Amin Al'bertovich Irgaliev, and Vladislav Dmitrievich Tarakanov. "Simulation of process of symbol recognition in regulating documents of organization." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2023, no. 2 (2023): 85–92. http://dx.doi.org/10.24143/2072-9502-2023-2-85-92.
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Improving the quality of classification of different documents is a purpose of modeling the optical character recognition. Non-digital documents, such as scanned or photographed documents, are difficult to classify correctly in electronic document management systems. A decision was made to simulate the process of optical character recognition in the regulatory documents of the organization. There have been considered various methods of modeling the process. The structure of departments for the electronic document management system is given. Methods of implementing optical character recognition (OCR) are considered. The stages of the OCR system development are revealed: image processing, segmentation, recognition. The methods of image processing are analyzed. The main processes associated with image processing are disclosed: alignment, blurring, binarization, finding contours, removing extra lines. Comparison of image blur methods is made. Two stages of image binarization are defined: conversion of a color image into a gray image, binarization of a gray image. The Kenny operator is proposed as a second stage of binarization, which is used to detect the boundaries of the image. The last stage of image processing is the process of removing extra lines. Algorithms for dividing text areas into segments are considered. 3 stages of segmentation are identified: string segmentation, word segmentation, character segmentation. A segmentation algorithm is defined based on calculating the average brightness of image pixels to search for different intervals: line spacing, word spacing, character spacing. Available popular online OCR services as well as some popular desktop programs are considered. A connection has been found between an artificial neural network and optical object recognition. To implement the recognition stage, it is proposed to use an artificial neural network.
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B, Rithik, Raghav G, Harshith M, Rahul Patwadi, and Aravind H S. "Licence Plate Recognition System Using Open-CV and Tesseract OCR Engine." International Journal of Engineering Research in Computer Science and Engineering 9, no. 9 (2022): 8–12. http://dx.doi.org/10.36647/ijercse/09.09.art003.
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As the technology has taken a leap to make sure human lives get easier, it has also come with certain consequences. One of them being traffic control and vehicle owner identification has really become a serious issue in the 21st century. Due to the advancements in automobile technology, it is very easy for a person to violate traffic rules and it is practically not possible for humans to stop or have a track record of the vehicles’ number plate travelling at higher speeds. This is a major problem which is being faced by developing countries and our paper will discuss an implementable solution for this problem. Licence plate recognition (LPR) is an information processing system which performs an optical character recognition (OCR) on a digital image of the licence plate which consists of alpha-numeric characters. In this paper we put forward three staged licence plate recognition system based on open-cv and tesseract OCR engine which consists of licence plate detection, character segmentation and character recognition. The system generally uses infrared (IR) illumination to allow the camera to capture images at any point of the day. It also performs various functions such as capturing the image of the vehicle, storing the captured image along with the transcript of the licence plate. Open cv plays an important role in preparing images and videos to identify objects and tesseract OCR is used for text recognition in our prototype. The main purpose of this system is to design and develop an accurate image processing method along with successful recognition of the alphanumeric characters.
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Baruah, Priyankush Kaushik, and Dr Pranabjyoti Haloi. "Development and Implementation of a Custom License Plate Detection and Recognition System Using YOLOv10 and Tesseract OCR: A Comprehensive Study in Computer Vision and Optical Character Recognition Technologies." International Journal of Innovative Technology and Exploring Engineering 14, no. 6 (2025): 20–26. https://doi.org/10.35940/ijitee.e1083.14060525.
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This study presents an automated license plate detection and recognition system, combining YOLOv10 for Realtime object detection and Tesseract OCR for robust text extraction. The methodology involves training a customised YOLOv10 model on annotated vehicle datasets to localize license plates, followed by region-of-interest (ROI) filtering to enhance accuracy. Detected plates are processed with Tesseract OCR to convert visual data into machine-readable text. Evaluated using precision, recall, and inference speed metrics, the system achieves 97 Parsant detection accuracy and real-time performance, demonstrating reliability in automated vehicle identification tasks such as traffic monitoring. This work underscores the synergy of YOLOv10s detection efficiency and Tesseracts OCR capabilities, offering a scalable solution for intelligent transportation systems.
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Rani, N. Shobha, Sanjay Kumar Verma, and Anitta Joseph. "A Zone Based Approach for Classification and Recognition Of Telugu Handwritten Characters." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (2016): 1647. http://dx.doi.org/10.11591/ijece.v6i4.10553.
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Realization of high accuracies and efficiencies in South Indian character recognition systems is one of the principle goals to be attempted time after time so as to promote the usage of optical character recognition (OCR) for South Indian languages like Telugu. The process of character recognition comprises pre-processing, segmentation, feature extraction, classification and recognition. The feature extraction stage is meant for uniquely recognizing each character image for the purpose of classifying it. The selection of a feature extraction algorithm is very critical and important for any image processing application and mostly of the times it is directly proportional to the type of the image objects that we have to identify. For optical technologies like South Indian OCR, the feature extraction technique plays a very vital role in accuracy of recognition due to the huge character sets. In this work we mainly focus on evaluating the performance of various feature extraction techniques with respect to Telugu character recognition systems and analyze its efficiencies and accuracies in recognition of Telugu character set.
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Rani, N. Shobha, Sanjay Kumar Verma, and Anitta Joseph. "A Zone Based Approach for Classification and Recognition Of Telugu Handwritten Characters." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (2016): 1647. http://dx.doi.org/10.11591/ijece.v6i4.pp1647-1653.
Full textAbstract:
Realization of high accuracies and efficiencies in South Indian character recognition systems is one of the principle goals to be attempted time after time so as to promote the usage of optical character recognition (OCR) for South Indian languages like Telugu. The process of character recognition comprises pre-processing, segmentation, feature extraction, classification and recognition. The feature extraction stage is meant for uniquely recognizing each character image for the purpose of classifying it. The selection of a feature extraction algorithm is very critical and important for any image processing application and mostly of the times it is directly proportional to the type of the image objects that we have to identify. For optical technologies like South Indian OCR, the feature extraction technique plays a very vital role in accuracy of recognition due to the huge character sets. In this work we mainly focus on evaluating the performance of various feature extraction techniques with respect to Telugu character recognition systems and analyze its efficiencies and accuracies in recognition of Telugu character set.
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Abish, Raj. M. S., Kumar. A. S. Manoj, and V. Murali. "Smart Assistant for Blind Humans using Rashberry PI." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 1712–18. https://doi.org/10.31142/ijtsrd11498.
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An OCR Optical Character Recognition system which is a branch of computer vision and in turn a sub class of Artificial Intelligence. Optical character recognition is the translation of optically scanned bitmaps of printed or hand written text into audio output by using of Raspberry pi. OCRs developed for many world languages are already under efficient use. This method extracts moving object region by a mixture of Gaussians based background subtraction method. A text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object, a text localization and Tesseract algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. Text characters in the localized text regions are then binaries and recognized by off the shelf optical character recognition software. The recognized text codes are output to blind users in speech. Performance of the proposed text localization algorithm. As the recognition process is completed, the character codes in the text file are processed using Raspberry pi device on which recognize character using Tesseract algorithm and python programming, the audio output is listed. Abish Raj. M. S | Manoj Kumar. A. S | Murali. V "Smart Assistant for Blind Humans using Rashberry PI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: https://www.ijtsrd.com/papers/ijtsrd11498.pdf
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Priyankush, Kaushik Baruah. "Development and Implementation of a Custom License Plate Detection and Recognition System Using YOLOv10 and Tesseract OCR: A Comprehensive Study in Computer Vision and Optical Character Recognition Technologies." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 14, no. 6 (2025): 20–26. https://doi.org/10.35940/ijitee.E1083.14060525.
Full textAbstract:
<strong>Abstract: </strong>This study presents an automated license plate detection and recognition system, combining YOLOv10 for realtime object detection and Tesseract OCR for robust text extraction. The methodology involves training a customised YOLOv10 model on annotated vehicle datasets to localize license plates, followed by region-of-interest (ROI) filtering to enhance accuracy. Detected plates are processed with Tesseract OCR to convert visual data into machine-readable text. Evaluated using precision, recall, and inference speed metrics, the system achieves 97% detection accuracy and real-time performance, demonstrating reliability in automated vehicle identification tasks such as traffic monitoring. This work underscores the synergy of YOLOv10’s detection efficiency and Tesseract’s OCR capabilities, offering a scalable solution for intelligent transportation systems.
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Priyankush, Kaushik Baruah. "Development and Implementation of a Custom License Plate Detection and Recognition System Using YOLOv10 and Tesseract OCR: A Comprehensive Study in Computer Vision and Optical Character Recognition Technologies." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 14, no. 6 (2025): 20–26. https://doi.org/10.35940/ijitee.E1083.14060525/.
Full textAbstract:
<strong>Abstract: </strong>This study presents an automated license plate detection and recognition system, combining YOLOv10 for realtime object detection and Tesseract OCR for robust text extraction. The methodology involves training a customised YOLOv10 model on annotated vehicle datasets to localize license plates, followed by region-of-interest (ROI) filtering to enhance accuracy. Detected plates are processed with Tesseract OCR to convert visual data into machine-readable text. Evaluated using precision, recall, and inference speed metrics, the system achieves 97% detection accuracy and real-time performance, demonstrating reliability in automated vehicle identification tasks such as traffic monitoring. This work underscores the synergy of YOLOv10’s detection efficiency and Tesseract’s OCR capabilities, offering a scalable solution for intelligent transportation systems.
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D, Anjana, Ajay k S, Akash k, Chaithanya C, Dr V. Balamurugan, and Divya mohan. "LOW COST SMART GLASSES FOR VISUALLY IMPAIRED USING RASPBERRY PI." International Journal of Engineering Applied Sciences and Technology 09, no. 06 (2024): 145–48. https://doi.org/10.33564/ijeast.2024.v09i06.018.
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Blindness is the inability to perceive light, leading individuals to rely on guiding tools and human assistance. This study introduces a prototype of smart glasses designed to help blind users by detecting objects and text signs, providing audio feedback. The device uses a Raspberry Pi as the microprocessor and a camera for detection. Object detection is powered by the YOLO algorithm, while Optical Character Recognition(OCR) identifies both handwritten and printed text. MATLAB is employed for OCR tasks, which include image capturing, text extraction, and text-to-speech conversion. The prototype can detect up to 15 objects and operates effectively under varying light conditions. The system can use voice responds to visually impaired people about the objects in front of them by uploading the photos to our backend object detection system through the camera function of smart glasses and then download the text descriptions of the result and then use the text-to-speech function . The average processing time for the prototype is about 1.9 seconds while walking quickly and 1.7 seconds at a slower pace.
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Navya, Koye, Mallela Sowmyah, Shaik Ayesha Amreen, Vemireddy Sravani, Yarrakula Gayathri Devi, and Ms Perli Nava Bhanu. "A Machine Learning Approach for Air Writing Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 3528–35. http://dx.doi.org/10.22214/ijraset.2023.50991.
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Abstract: Air Writing Recognition is a step towards the safety of people. Aim of this project is to make a step towards smart technology and provide an alternative when phones are unable to use. It helps the women, children and citizens in emergency situation. By writing a character (h, p) in air infront of CCTV, it recognizes that someone needs help. This project also send a voice message, text message to get emergency help and support. We hope that this project makes more safety to women, children and citizens and makes the crime rate less. The Air Writing Recognition project is a combination of computer vision object tracking and handwriting recognition. The Air Writing Recognition system uses the webcam of a computer to track character digits written in the air by the user, then uses a optical character recognition(OCR) algorithm to classify the character digits. And further it uses a Twilio account to make calls and sends message depends upon the character that the system recognized
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Kern, John, Claudio Urrea, Francisco Cubillos, and Ricardo Navarrete. "A Bio-Inspired Retinal Model as a Prefiltering Step Applied to Letter and Number Recognition on Chilean Vehicle License Plates." Applied Sciences 14, no. 12 (2024): 5011. http://dx.doi.org/10.3390/app14125011.
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This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others.
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Wahyu Andi Saputra, Muhammad Zidny Naf’an, and Asyhar Nurrochman. "Implementasi Keras Library dan Convolutional Neural Network Pada Konversi Formulir Pendaftaran Siswa." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 3, no. 3 (2019): 524–31. http://dx.doi.org/10.29207/resti.v3i3.1338.
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Form sheet is an instrument to collect someone’s information and in most cases it is used in a registration or submission process. The challenge being faced by physical form sheet (e.g. paper) is how to convert its content into digital form. As a part of study of computer vision, Optical Character Recognition (OCR) recently utilized to identify hand-written character by learning pattern characteristics of an object. In this research, OCR is implemented to facilitate the conversion of paper-based form sheet's content to be stored properly into digital storage. In order to recognize the character's pattern, this research develops training and testing method in a Convolutional Neural Network (CNN) environment. There are 262.924 images of hand-written character sample and 29 paper-based form sheets from SDN 01 Gumilir Cilacap that implemented in this research. The form sheets also contain various sample of human-based hand-written character. From the early experiment, this research results 92% of accuracy and 23% of loss. However, as the model is implemented to the real form sheets, it obtains average accuracy value of 63%. It is caused by several factors that related to character's morphological feature. From the conducted research, it is expected that conversion of hand-written form sheets become effortless.
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Naik, Shivam, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, and Muhammad Zeshan Afzal. "Investigating Attention Mechanism for Page Object Detection in Document Images." Applied Sciences 12, no. 15 (2022): 7486. http://dx.doi.org/10.3390/app12157486.
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Page object detection in scanned document images is a complex task due to varying document layouts and diverse page objects. In the past, traditional methods such as Optical Character Recognition (OCR)-based techniques have been employed to extract textual information. However, these methods fail to comprehend complex page objects such as tables and figures. This paper addresses the localization problem and classification of graphical objects that visually summarize vital information in documents. Furthermore, this work examines the benefit of incorporating attention mechanisms in different object detection networks to perform page object detection on scanned document images. The model is designed with a Pytorch-based framework called Detectron2. The proposed pipelines can be optimized end-to-end and exhaustively evaluated on publicly available datasets such as DocBank, PublayNet, and IIIT-AR-13K. The achieved results reflect the effectiveness of incorporating the attention mechanism for page object detection in documents.
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Susilawati, Helfy, Sifa Nurpadillah, Wahju Sediono, and Agung Ihwan Nurdin. "Design of a road marking violation detection system at railway level crossings." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 883. http://dx.doi.org/10.11591/ijece.v15i1.pp883-893.
Full textAbstract:
When a train passed through a railway-level crossing, a common phenomenon was that many vehicles attempted to overtake others by crossing into lanes designated for oncoming traffic, resulting in both roads becoming congested with motorized vehicles. At that time, no system was in place to enforce penalties for violating road markings at level crossings. Therefore, a system capable of detecting such violations when trains pass through was needed. The designed system utilized a Raspberry Pi 4, a webcam, and an ultrasonic sensor. The single shot detector (SSD) method was employed for vehicle classification. The optical character recognition (OCR) method was used for character recognition on license plates. The research involved object detection at level crossings using varied objects (cars and motorcycles) with license plates categorized into two types: white background plates with black numbers and black background plates with white numbers. Based on the research results, turning on the webcam when the bar opened and closed using an ultrasonic sensor got an average error of 0.573% and 0.582%. The system could distinguish objects with an average recognition delay of 0.554 seconds and 0.702 seconds for car and motorbike objects. Regarding number plate detection, the success rate of character recognition stood at 64.45%.
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Salsabila, Nurul, and Sriani Sriani. "Enhancing Automated Vehicle License Plate Recognition with YOLOv8 and EasyOCR." Journal of Information Systems and Informatics 6, no. 3 (2024): 1577–97. http://dx.doi.org/10.51519/journalisi.v6i3.848.
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This research focuses on the development of an automatic system for vehicle license plate recognition using YOLOv8, EasyOCR, and CNN methods for object classification. The main issue raised is the need for an accurate and efficient system for recognizing vehicle license plates in real-time in dynamic environments, especially in urban areas with high traffic levels. The method used in this study involves resizing the input image to 416x416 pixels to standardize the data, analyzing the YOLO architecture that divides the image into a 7x7 grid, and using the Convolutional Neural Network (CNN) algorithm for feature extraction and object classification. Object detection uses the YOLOv8 method which is tasked with recognizing license plates using a previously trained YOLO (pretrained model) model then implemented and tested using video with 4k quality to ensure its effectiveness in detecting vehicle license plate objects, followed by the Optical Character Recognition (OCR) process with the EasyOCR method to read text on license plates and tested to ensure its effectiveness in reading characters on license plates vehicle number. The purpose of this research is to develop a system that can improve accuracy and efficiency in vehicle license plate recognition. The results show that the accuracy, precision, recall and F1-Score for object detection reach 100% and the average percentage of detected text conformity is 74.66%, which shows that this system is reliable in real applications and contributes to the development of automatic license plate recognition technology.
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P, Arun. "Automatic Traffic Rules Violation Detection and Number Plate Recognition." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45880.
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Abstract—As road traffic continues to grow and traffic rule violations become more common, there's a growing need for smart systems that can automatically detect and report such incidents. This project introduces an automated solution for detecting traffic violations and recognizing license plates using deep learning, convolutional neural networks (CNNs), and optical character recognition (OCR). The system is capable of identifying common offenses such as riding without a helmet, triple riding, and running a red light, by analyzing real-time video footage. Using object detection models like YOLO, it accurately spots violators and isolates the license plate area from the video frames. Tools like EasyOCR are then used to read and extract the alphanumeric details from the plates. Once a violation is detected, the system automatically sends an email—with the offense details and a snapshot of the incident—using Python’s smtplib library. This solution is designed to support traffic enforcement agencies by making violation tracking more efficient, reliable, and automated.. Keywords—Deep Learning,CNN and OCR..
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Satya, Barka, Danny Manongga, Hendry, and Afrig Aminuddin. "Optimized YOLOv8 for Automatic License Plate Recognition on Resource Constrained Devices." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 21976–81. https://doi.org/10.48084/etasr.9983.
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This paper presents an optimized Automatic License Plate Recognition (ALPR) system designed for resource-constrained devices, leveraging YOLOv8 for real-time object detection and Optical Character Recognition (OCR) to extract license plate information under challenging conditions such as low-light, motion blur, and occlusions. Unlike traditional ALPR systems that rely on high computational resources, our approach balances detection accuracy, processing speed, and efficiency. The system is evaluated on three benchmark datasets: the Chinese City Parking Dataset (CCPD) with 250,000 images under diverse conditions, the UFPR-ALPR Dataset (Universidade Federal do Paraná, Brazil) containing 4,500 real-world traffic images, and the RodoSol-ALPR Dataset with 20,000 highway surveillance images for high-speed license plate recognition. Among various YOLOv8 variants tested, YOLOv8-s achieved the best performance, with a mean Average Precision (mAP) of 99.3% while sustaining over 30 Frames Per Second (FPS), making it suitable for real-time ALPR applications. Furthermore, image sharpening and contour segmentation techniques improved OCR recognition accuracy by 5.1% under low-light conditions, improving robustness. Comparative analysis against state-of-the-art OCR-based ALPR methods (EasyOCR, FastOCR, and CR-NET) demonstrated that our approach surpasses existing models in both recognition rate and computational efficiency. These findings establish YOLOv8 as a highly effective and deployable solution for intelligent transportation, surveillance, and law enforcement applications requiring real-time license plate recognition with minimal computational overhead.
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Filsa, Nisfal, Widodo, and Bambang Prasetya Adhi. "Kinerja Algoritma Canny untuk Mendeteksi Tepi dalam Mengidentifikasi Tulisan pada Citra Digital Meme." PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer 3, no. 1 (2019): 45–53. http://dx.doi.org/10.21009/pinter.3.1.8.
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Citra digital Meme merupakan sarana penyampaian informasi, teks pada Meme sebagian besar akan bergabung dengan latar pada gambar. Untuk membedakan latar dan teks dapat dilakukan dengan deteksi Tepi. Algoritma Canny merupakan salah satu algoritma deteksi Tepi yang memiliki tingkat kesalahan yang minimum dan menghasilkan citra tepian yang optimal. Salah satu penggunaan deteksi Tepi dapat diterapkan citra digital Meme untuk menentukan wilayah teks yang terdapat pada citra Meme. Hasil algoritma Canny mendeteksi Tepi untuk menentukan wilayah tulisan pada Meme lalu diidentifkiasi menggunakan pengenalan karakter optis (OCR) akan dijadikan perhitungan untuk menilai kinerja algoritma deteksi Tepi Canny. Kinerja algoritma Canny mendeteksi Tepi untuk menentukan wilayah kandidat teks meningkatkan akurasi deteksi tulisan pada OCR (Object Character Recognition) dengan akurasi keberhasilan secara keseluruhan sebesar 65,47% dibandingkan dengan deteksi tulisan langsung menggunakan OCR sebesar 47,91%. Selain itu mengurangi tingkat kesalahan deteksi tulisan pada OCR dengan akurasi kesalahan secara keseluruhan yaitu kehilangan karakter sebesar 34,53% dan kelebihan karakter sebesar 35,98% dibandingkan deteksi tulisan langsung menggunakan OCR dengan akurasi kehilangan karakter sebesar 52,09% dan kelebihan karakter sebesar 52,62. Kinerja algoritma Canny mendeteksi wilayah kandidat teks pada OCR secara keseluruhan meningkatkan akurasi kebenaran dalam mendeteksi tulisan pada citra digital Meme dan mengurangi persentase kesalahan.
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Mr. S. Suresh, J Gopalakrishnan, K Guruseelan, S Giridharan, and K Gururajan. "Traffic Violation Detection Using Deep Learning." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1359–63. https://doi.org/10.47392/irjaem.2025.0221.
Full textAbstract:
Traffic violation detection is a crucial aspect of intelligent transportation systems, enabling automated identification of vehicles for security, law enforcement, and toll collection. This process involves image acquisition, pre-processing, segmentation, feature extraction, and character recognition. Various techniques, including edge detection, morphological operations, and deep learning-based object detection models, enhance accuracy and robustness. Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO) models have significantly improved real-time detection performance. Challenges such as varying lighting conditions, occlusions, and diverse plate formats necessitate adaptive algorithms. Optical Character Recognition (OCR) is employed to extract alphanumeric details. Machine learning and deep learning techniques refine detection precision. Integration with cloud computing and IoT enhances scalability and deployment. Future advancements focus on improving accuracy, speed, and adaptability to complex environments.
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Susilawati, Helfy, Sifa Nurpadillah, Wahju Sediono, and Agung Ihwan Nurdin. "Design of a road marking violation detection system at railway level crossings." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 883–93. https://doi.org/10.11591/ijece.v15i1.pp883-893.
Full textAbstract:
When a train passed through a railway-level crossing, a common phenomenon was that many vehicles attempted to overtake others by crossing into lanes designated for oncoming traffic, resulting in both roads becoming congested with motorized vehicles. At that time, no system was in place to enforce penalties for violating road markings at level crossings. Therefore, a system capable of detecting such violations when trains pass through was needed. The designed system utilized a Raspberry Pi 4, a webcam, and an ultrasonic sensor. The single shot detector (SSD) method was employed for vehicle classification. The optical character recognition (OCR) method was used for character recognition on license plates. The research involved object detection at level crossings using varied objects (cars and motorcycles) with license plates categorized into two types: whitebackground plates with black numbers and black background plates with white numbers. Based on the research results, turning on the webcam when the bar opened and closed using an ultrasonic sensor got an average error of 0.573% and 0.582%. The system could distinguish objects with an average recognition delay of 0.554 seconds and 0.702 seconds for car and motorbikeobjects. Regarding number plate detection, the success rate of character recognition stood at 64.45%.
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Swanand Joshi, Pramod Jejure, Chatrasal Jadhav, and Vishal Jankar. "Automatic Number Plate Recognition Using YOLOv8 Model." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 1088–97. https://doi.org/10.32628/ijsrst251222657.
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Automatic Number Plate Recognition (ANPR) systems have become a critical tool in various sectors, including traffic management, law enforcement, and tolling systems. This paper presents an in-depth exploration of an advanced ANPR framework that leverages cutting-edge image processing methodologies and machine learning models to deliver exceptional accuracy in license plate detection and recognition. The system follows a multi-phase approach encompassing image capture, preprocessing, plate localization, character segmentation, and optical character recognition (OCR). Notably, the integration of YOLOv8, a state-of-the-art deep learning model for object detection, significantly enhances the feature extraction and classification process, boosting the system's performance across diverse environmental challenges. The proposed approach achieves a recognition accuracy exceeding 95%, highlighting its potential for deployment in real-world scenarios. Additionally, the paper addresses various challenges encountered in ANPR systems, such as variations in license plate formats, fluctuating lighting conditions, and partial occlusions, and proposes future research directions aimed at further improving robustness and operational efficiency.
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Rai, Laxmisha, and Hong Li. "MyOcrTool: Visualization System for Generating Associative Images of Chinese Characters in Smart Devices." Complexity 2021 (May 7, 2021): 1–14. http://dx.doi.org/10.1155/2021/5583287.
Full textAbstract:
Majority of Chinese characters are pictographic characters with strong associative ability and when a character appears for Chinese readers, they usually associate with the objects, or actions related to the character immediately. Having this background, we propose a system to visualize the simplified Chinese characters, so that developing any skills of either reading or writing Chinese characters is not necessary. Considering the extensive use and application of mobile devices, automatic identification of Chinese characters and display of associative images are made possible in smart devices to facilitate quick overview of a Chinese text. This work is of practical significance considering the research and development of real-time Chinese text recognition, display of associative images and for such users who would like to visualize the text with only images. The proposed Chinese character recognition system and visualization tool is named as MyOcrTool and developed for Android platform. The application recognizes the Chinese characters through OCR engine, and uses the internal voice playback interface to realize the audio functions and display the visual images of Chinese characters in real-time.
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V, Mohanapriya. "YOLO and OCR-Based Automated Identity Document Verification." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1907–12. https://doi.org/10.22214/ijraset.2025.68628.
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Abstract: Ensuring the authenticity of identity documents is crucial for secure digital transactions and regulatorycompliance. This paperpresentsa Comprehensive AutomatedDocumentVerificationSystemthatutilizesYOLO (YouOnlyLookOnce)for objectdetection andOCR(Optical Character Recognition) for dataextraction toverifyAadhaar cards,PANcards, and Voter IDcards. The systemautomates the verification process by detecting key document features, extractingrelevanttextualdata, andcross-verifyingitagainst predefined templates and databases. By integrating deep learning-based object detection with OCR, the proposed solution achieves high accuracy, efficiency, and scalability, reducing reliance on manual verification and minimizing fraud risks. Experimental results demonstrate the system’s robustness in detecting forged or tampered documents. This research contributes to improving digital security and streamlining identity verification in sectors such as banking, government services, and online KYC processes.
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Divya, E., V. Jaishreenithi, S. Keerthika, and Yamuna S. "Image based product recognition using barcode and OCR for visually impaired people." World Journal of Advanced Research and Reviews 14, no. 3 (2022): 473–76. https://doi.org/10.5281/zenodo.7731779.
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Humans do require a lot of communication. When there is interaction between normal people and blind people, communication can be difficult at certain points. This program facilitates communication between blind persons and the rest of the world. Numerous programs are available in the marketplace to help blind people interact with the outside world. To reach the blind, voice mail and chat programs are available. This program allows a person who is blind or has a visual impairment to purchase a product at a supermarket or grocery store using a scanner to scan the barcode of the product. It helps blind people get information on packaged foods and their descriptions. To use this system, you only need to take a photo of the object.
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Park and Won-Hyuk Choi, Je-Hong, and Min-Seok Jie. "Aircraft Detection and Registration Number Recognition System with YOLO and OCR." Journal of Neonatal Surgery 14, no. 2 (2025): 69–77. https://doi.org/10.52783/jns.v14.1719.
Full textAbstract:
Recently, large airports are promoting a smart airport business that introduces robots and IoT using artificial intelligence and big data, which are major technologies in the fourth industrial era. Various artificial intelligence technologies are applied not only to customer convenience but also to airport security and control, and in particular, video monitoring and analysis technologies such as missing children are introduced through intelligent CCTV. In this paper, we propose a system for recognizing aircraft detection and registration number (tail number) taking off and landing on the runway using YOLO, a deep learning object detection model, and character recognition technology (OCR). It acquires aircraft data through cameras installed on the ground and learns it with the fastest YOLO model among deep learning object detection models to automatically detect aircraft in the airport and its registration number area. And the registration number recognized the result detected by YOLO using the OCR algorithm through the image preprocessing process. This study conducted data acquisition and real-time detection tests at Taean Airfield (RKTA) at Hanseo University in Korea, and real-time aircraft detection was more than 90% and registration number recognition was more than 80%. Through this system, information on the direction, location, model, and registration number of the aircraft can be acquired, confirming its utility as an automatic ground monitoring system for small airports in the future.
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Jain, Mamata Kishor. "Smart Eye for Visually Impaired People." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 2387–91. http://dx.doi.org/10.22214/ijraset.2023.57531.
Full textAbstract:
Abstract: This paper presents a novel mobile application tailored to enhance the daily experiences of individuals with visual impairments. Leveraging cutting-edge real-time object detection and Optical Character Recognition (OCR) techniques, the app, constructed using the TensorFlow algorithm and the comprehensive Common Objects in Context (COCO) dataset, aims to empower users by providing invaluable assistance through their mobile devices. The application utilizes the device's camera to seamlessly identify objects in real-time via the object detection algorithm, while simultaneously extracting and vocalizing text from captured images through OCR technology. A crucial aspect of the app lies in its commitment to user-friendliness, ensuring a straightforward interface tailored specifically for the needs of visually impaired individuals. The efficacy of the system was rigorously evaluated, focusing on both accuracy and real-time performance. Results conclusively demonstrate the app's effectiveness and efficiency in offering essential support to visually impaired users, marking a significant stride toward fostering independence and ease in their daily lives. Remarkably, this mobile application stands out for its affordability and accessibility, providing a practical and inclusive solution for individuals with visual impairments. By delivering audio feedback about identified objects and recognized text, this innovative app contributes to a more accessible and autonomous lifestyle for its users, contributing positively to the broader landscape of assistive technology for the visually impaired.