Relevant bibliographies by topics / Object Character Recognition (OCR)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Object Character Recognition (OCR)'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Object Character Recognition (OCR)"

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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 ima
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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 v
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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, ob
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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
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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 objec
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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
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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,
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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
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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 U
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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
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Dissertations / Theses on the topic "Object Character Recognition (OCR)"

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Lamberti, Lorenzo. "A deep learning solution for industrial OCR applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19777/.

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This thesis describes a project developed throughout a six months internship in the Machine Vision Laboratory of Datalogic based in Pasadena, California. The project aims to develop a deep learning system as a possible solution for industrial optical character recognition applications. In particular, the focus falls on a specific algorithm called You Only Look Once (YOLO), which is a general-purpose object detector based on convolutional neural networks that currently offers state-of-the-art performances in terms of trade-off between speed and accuracy. This algorithm is indeed well known fo
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McDonald, Mercedes Terre. "OCR: A STATISTICAL MODEL OF MULTI-ENGINE OCR SYSTEMS." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4459.

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This thesis is a benchmark performed on three commercial Optical Character Recognition (OCR) engines. The purpose of this benchmark is to characterize the performance of the OCR engines with emphasis on the correlation of errors between each engine. The benchmarks are performed for the evaluation of the effect of a multi-OCR system employing a voting scheme to increase overall recognition accuracy. This is desirable since currently OCR systems are still unable to recognize characters with 100% accuracy. The existing error rates of OCR engines pose a major problem for applications where a singl
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Granlund, Oskar, and Kai Böhrnsen. "Improving character recognition by thresholding natural images." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208899.

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The current state of the art optical character recognition (OCR) algorithms are capable of extracting text from images in predefined conditions. OCR is extremely reliable for interpreting machine-written text with minimal distortions, but images taken in a natural scene are still challenging. In recent years the topic of improving recognition rates in natural images has gained interest because more powerful handheld devices are used. The main problem faced dealing with recognition in natural images are distortions like illuminations, font textures, and complex backgrounds. Different preprocess
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Mishra, Vishal Vijayshankar. "Sequence-to-Sequence Learning using Deep Learning for Optical Character Recognition (OCR)." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1513273051760905.

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Rodrigues, Antonio Jose Nunes Navarro. "A robust off-line hand written character recognition system using dynamic features." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295503.

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Sandgren, Frida. "Creation of a customised character recognition application." Thesis, Uppsala University, Department of Linguistics and Philology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4801.

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<p>This master’s thesis describes the work in creating a customised optical character recognition (OCR) application; intended for use in digitisation of theses submitted to the Uppsala University in the 18th and 19th centuries. For this purpose, an open source software called Gamera has been used for recognition and classification of the characters in the documents. The software provides specific algorithms for analysis of heritage documents and is designed to be used as a tool for creating domain-specific (i.e. customised) recognition applications.</p><p>By using the Gamera classifier trainin
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Monger, David M. "The human factors aspects of interactive document image description for OCR of handwritten forms." Thesis, University of Essex, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238747.

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Radvar-Zanganeh, Siasb. "The role of the Elementary Perceiver and Memorizer (EPAM) in optical character recognition (OCR)." Thesis, Connect to online version, 1994. http://0-wwwlib.umi.com.mercury.concordia.ca/cr/concordia/fullcit?pMM10888.

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Favish, Ashleigh. "Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR)." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31389.

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The impact of apartheid on land registration is still evident within South Africa. The Deeds Registry is facing a current backlog in registering an estimated 900,000 title deeds. Providing formal ownership, through title, is seen as necessary for unlocking the 'dead capital’ of unregistered property, fostering access to capital markets and poverty alleviation. Within the current legislative framework, the Deeds Registry only accepts paper documents, which introduces inefficiencies. To increase the number of deeds processed per day, automation of manual data capture is tested using an OCR pipel
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Serafini, Sara. "Machine Learning applied to OCR tasks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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The content of this thesis describes the work done during a six-month internship at Datalogic, in its research laboratories in Pasadena (CA). The aim of my research was to implement and evaluate a classifier as part of an industrial OCR system for learning purposes and to see how well it could work in comparison to current best Datalogic products, since it might be simpler/faster, it might be a good alternative for implementing on an embedded system (where current Datalogic products may not be able to run fast enough).
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Books on the topic "Object Character Recognition (OCR)"

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National Bureau of Standards. Optical character recognition (OCR)--Dot matrix character sets for OCR-MA. U.S. Dept. of Commerce/National Bureau of Standards, 1987.

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Rice, Stephen V. Optical character recognition: An illustrated guide to the frontier. Kluwer Academic Publishers, 1999.

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Guide To Ocr For Arabic Scripts. Springer, 2012.

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OCR with a smile!: An operator's guide to optical character recognition. House of Scanning, 1998.

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Guide To Ocr For Indic Scripts Document Recognition And Retrieval. Springer, 2009.

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Wang, Patrick S. Handbook of Character Recognition and Document Image Analysis. World Scientific Publishing Co Pte Ltd, 1997.

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(Editor), H. Bunke, and P. S. P. Wang (Editor), eds. Handbook of Character Recognition and Document Image Analysis. World Scientific Publishing Company, 1997.

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Märgner, Volker, and Haikal El Abed. Guide to OCR for Arabic Scripts. Springer, 2014.

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Märgner, Volker, and Haikal El Abed. Guide to OCR for Arabic Scripts. Springer, 2012.

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Rice, Stephen V., George Nagy, and Thomas A. Nartker. Optical Character Recognition: An Illustrated Guide to the Frontier (The Springer International Series in Engineering and Computer Science). Springer, 1999.

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Book chapters on the topic "Object Character Recognition (OCR)"

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Bennamoun, M., and G. J. Mamic. "Optical Character Recognition." In Object Recognition. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1_5.

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Klauer, B., K. Waldschmidt, and R. Heinrich. "An Object-Oriented Character Recognition Engine." In Informatik aktuell. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78565-8_17.

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Kumar, Munish, M. K. Jindal, and R. K. Sharma. "Review on OCR for Handwritten Indian Scripts Character Recognition." In Advances in Digital Image Processing and Information Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24055-3_28.

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Dreuw, Philippe, David Rybach, Georg Heigold, and Hermann Ney. "RWTH OCR: A Large Vocabulary Optical Character Recognition System for Arabic Scripts." In Guide to OCR for Arabic Scripts. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4072-6_9.

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Madarász, Gábor, Noémi Ligeti-Nagy, András Holl, and Tamás Váradi. "OCR Cleaning of Scientific Texts with LLMs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65794-8_4.

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AbstractCorrecting Optical Character Recognition (OCR) errors is a major challenge in preprocessing datasets consisting of legacy PDF files. In this study, we develop Large Language Models specially finetuned to correct OCR errors. We experimented with the mT5 model (both the mT5-small and mT5-large configurations), a Text-to-Text Transfer Transformer-based machine translation model, for the post-correction of texts with OCR errors. We compiled a parallel corpus consisting of text corrupted with OCR errors as well as corresponding clean data. Our findings suggest that the mT5 model can be successfully applied to OCR error correction with improving accuracy. The results affirm the mT5 model as an effective tool for OCR post-correction, with prospects for achieving greater efficiency in future research.
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Schlüter, Marian, Christian Tepper, Clemens Briese, Ole Kroeger, Raul Vicente-Garcia, and Jörg Krüger. "Deep Learning-Based Optical Character Recognition for Identifying On-Label Printed Part Numbers of Used Automotive Parts: A Comparative Study of Open Source and Commercial Methods." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-77429-4_58.

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AbstractThis paper explores the use of deep learning-based optical character recognition (OCR) to identify part numbers for used automotive parts. It compares open source and advanced AI methods to commercial tools from Google, Amazon, and Microsoft. The study finds that fine-tuned open source models outperform commercial services, especially for complex part numbers unrelated to any language structure. The preferred open source method, MaskedTextSpotter, is fine-tuned with image data from old vehicle and electrical parts, captured by a smartphone and 2D barcode scanner. Additionally, a new data augmentation method, CharChan, is introduced, replacing detected characters with random examples for better character recognition. The experiments demonstrate the efficacy of deep learning-based OCR for automotive part number identification.
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Torres, Weslley, Mark G. J. van den Brand, and Alexander Serebrenik. "Suitability of Optical Character Recognition (OCR) for Multi-domain Model Management." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58167-1_11.

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Emon, Md Imdadul Haque, Khondoker Nazia Iqbal, Md Humaion Kabir Mehedi, Mohammed Julfikar Ali Mahbub, and Annajiat Alim Rasel. "A Review of Optical Character Recognition (OCR) Techniques on Bengali Scripts." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25161-0_6.

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Vibhute, Pritish Mahendra, and Mangesh Sudhir Deshpande. "Optical Character Recognition (OCR) of Marathi Printed Documents Using Statistical Approach." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1810-8_49.

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Chen, Lu, Jiao Sun, and Wei Xu. "FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) Systems." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67664-3_33.

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Conference papers on the topic "Object Character Recognition (OCR)"

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Kaushik, Priyanka, Priyanka Rawat, Devyansh Batra, P. Vensheeba Delin, Saurabh Pratap Singh Rathore, and S. Kaliappan. "Python-Based Optical Character Recognition (OCR)." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10984764.

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Sarkar, Oshmita, Satyam Sinha, Ajay Kumar Jena, Ajaya Kumar Parida, Nirupama Parida, and Raj Kumar Parida. "Automatic Number Plate Character Recognition using Paddle-OCR." In 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET). IEEE, 2024. http://dx.doi.org/10.1109/icicet59348.2024.10616305.

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Pallempati, Ishita, D. Vijaya Lakshmi, and M. Swami Das. "Handwritten Character Recognition and Vehicle Number Recognition using OCR Method." In 2024 Third International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON). IEEE, 2024. https://doi.org/10.1109/teeccon64024.2024.10939189.

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Maruthiram, Katroth Balakrishna, and G. Venkata Rami Reddy. "Automation of the recognition of Optical Character Recognition (OCR) in handwritten documents." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10921836.

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Nandeshwar, Vikas, Harshad Sheelwant, Nishit Shelar, et al. "Smart Audio Description Glasses with Object Recognition and OCR." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714715.

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Aung, Thura, Ye Kyaw Thu, and Myat Noe Oo. "myOCR: Optical Character Recognition for Myanmar language with Post-OCR Error Correction." In 2024 19th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). IEEE, 2024. https://doi.org/10.1109/isai-nlp64410.2024.10799448.

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Deshmukh, Jawad, Sharique Shah, Asif Shaikh, Ashfan Bargir, and Salim Shaikh. "G2OCR: Integrating Speech Recognition and Optical Character Recognition(OCR) for Automated Transcription of Gujarati Audio-Visual Content." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866606.

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Peng, Jingshu. "Onstruction and Security Performance Analysis of an Anti-Attack Optical Character Recognition (OCR) System." In 2024 International Conference on Information Technology, Communication Ecosystem and Management (ITCEM). IEEE, 2024. https://doi.org/10.1109/itcem65710.2024.00034.

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Hasan, Md Sajid, Avi Pal, and Sk Md Masudul Ahsan. "Enhancing Bangla Language Text Recognition in OCR Using Levenshtein Distance and Character Grouping Strategy." In 2024 IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON). IEEE, 2024. https://doi.org/10.1109/spicscon64195.2024.10941489.

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Gupta, Unnati, Ayush Kumar, Arti Gupta, Gaurav Raj, and Arun Prakash Agrawal. "Advances in Handwritten Character Recognition: A Comparison of OCR and Large Language Model-Based Approaches." In 2024 International Conference on Emerging Technologies and Innovation for Sustainability (EmergIN). IEEE, 2024. https://doi.org/10.1109/emergin63207.2024.10961487.

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Reports on the topic "Object Character Recognition (OCR)"

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Kjersten, Brian. Arabic Optical Character Recognition (OCR) Evaluation in Order to Develop a Post-OCR Module. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada554465.

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Federal Information Processing Standards Publication: optical character recognition (OCR) - DOT matrix character sets for OCR-MA. National Bureau of Standards, 1987. http://dx.doi.org/10.6028/nbs.fips.129-1987.

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Federal Information Processing Standards Publication: for information systems - optical character recognition (OCR) - matrix character sets for OCR-MA. National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.fips.129.

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