Journal articles: 'Handwritten Devnagri character recognition'

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JYOTI, A.PATIL, and SANJAY R. PATIL DR. "OPTICAL HANDWRITTEN DEVNAGARI CHARACTER RECOGNITION USING ARTIFICIAL NEURAL NETWORK APPROACH." IJIERT - International Journal of Innovations in Engineering Research and Technology 5, no. 3 (2018): 67–71. https://doi.org/10.5281/zenodo.1454101.

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Character recognitions play a wide role in the fast moving world with the growing technology,by providing more scope to perform research in OCR techniques. In the field of pattern recognition Devnagari handwritten character recognition is one of the challenging research area. Character recognition is defined as electronic translation of scanned images of handwritten or printed text into a machine encoded text. In this paper proposed an off line handwritten Devnagari character recognition technique with the use of feed forward neural network. For training the neural network a handwritten Devnagari character which is resized into 20x30 pixels is used. The same character is then given to the neural network as input with different set of neurons in hidden layer after the training process,and their recognition accuracy rate is calculated and compared for different Devnagari characters. Good recognition accuracy rates has been given by the proposed system comparable to that of other hand written character recognition systems. https://www.ijiert.org/paper-details?paper_id=141157

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Yawalkar, Prashant, and M. U. Kharat. "A Hybrid Approach for Recognition of Hand Written Devnagri Compound Characters." Asian Journal of Computer Science and Technology 8, no. 2 (2019): 70–76. http://dx.doi.org/10.51983/ajcst-2019.8.2.2137.

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Being an effective tool in the world of communication, numerous techniques have been developed for documenting the handwritten text. Few of the exceptional techniques describe the environment of handwritten scripts and further convert it into electronic data by implementing various algorithms. Devnagri is one of the widely used scripts for most popular and commonly used languages like Marathi and Hindi. Recent development in the field of handwritten character recognition based on different methodologies like neural network, fuzzy logic, and deep neural networks has shown remarkable improvement in character recognition accuracy from 75% to 96%. We propose a fuzzy-Neural hybrid approach for recognition of hand written Devnagri compound character that uses a rotation invariant rule-based thinning algorithm as one of the major pre-processing activity. Thinning the characters to their central line, preserving the shape of the character are the distinctive features of thinning algorithm. Concurrent application of different rules to each pixel of the character image results into symmetrical thinning as well as improves the overall speed of the system. The system is trained using Neural Network where the weights are optimized using fuzzy rules improving the accuracy of the system.Results obtained for the fuzzy-neural based system with thinning helps in preserving the topology of the characters written in Devnagri and prove that accuracy of the system has stabilized in the band of 92-97% which was fluctuating in the band of 89-94% for the previously implemented systems. The system also shows a substantial improvement in accuracy for recognition of compound characters in comparison with our previously implemented system.

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Shaikh, Jiya. "Handwritten Devnagari Character and Joint Devnagari Character Recognition Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6877–81. http://dx.doi.org/10.22214/ijraset.2023.53240.

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Abstract: In our day to day life we use devnagari to communicate with each other verbally. There are many people in our country who still like to write their documents in devnagari only. In our project we recognizing devnagari as well as joint devnagari characters. The character images in our dataset are imposed by joint characters, this particular aspect leads to various conflicting behaviors of the recognition algorithm which in turn reduces the accuracy of recognition. The training of joint devnagari character image samples are carried out by using one of the deep convolution neural networks known as CNN. The handwritten datasets is collected artificially from users in the age range of 18–21, 22–25, and 26–30. It consists of joint devnagari text that are used to evaluate the experiment's performance. The datasets are comprised of many classes. Those classes include devnagari characters, devnagari digits as well as joint devnagari characters. After performing essential steps. It is observed that the performance of CNN Classifiers like Random Forest is overall high. An overall accuracy of 94% is achieved during the recognition of devnagari character set and an accuracy of over 90% is accomplished with respect to handwritten data samples with training and testing proportions of 70% and 30% in both of the cases for the number of classes of over 58

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Dongre, Vikas J., and Vijay H. Mankar. "Development of Comprehensive Devnagari Numeral and Character Database for Offline Handwritten Character Recognition." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–5. http://dx.doi.org/10.1155/2012/871834.

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In handwritten character recognition, benchmark database plays an important role in evaluating the performance of various algorithms and the results obtained by various researchers. In Devnagari script, there is lack of such official benchmark. This paper focuses on the generation of offline benchmark database for Devnagari handwritten numerals and characters. The present work generated 5137 and 20305 isolated samples for numeral and character database, respectively, from 750 writers of all ages, sex, education, and profession. The offline sample images are stored in TIFF image format as it occupies less memory. Also, the data is presented in binary level so that memory requirement is further reduced. It will facilitate research on handwriting recognition of Devnagari script through free access to the researchers.

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Singh, Pratibha, Ajay Verma, and Narendra S. Chaudhari. "Performance Evaluation of Classifiers Applying Directional Features for Devnagri Numeral Recognition." Advanced Materials Research 403-408 (November 2011): 1042–48. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1042.

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Handwriting recognition is a special category of pattern recognition which is matured enough for English language, but for Hindi it is in development state. Among various features directional features found to outperform than the others. So in this paper, we have evaluated the performance of various direction features and various classifiers for the handwritten Devnagri numeral recognition. The character image is preprocessed and portioned into sub-images. The standard zoning is compared against flexible zoning. An experimental comparison of gradient features and chain code histogram feature is evaluated with Bays classifier, K-nn, fuzzy k-nn. For comparison of the performance, the error rate and complexity of computation and time is used as the measure. Gradient features are found to outperform among various directional features.

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Agarwal, Shruti. "Offline Handwritten Character Recognition with Devnagari Script." IOSR Journal of Computer Engineering 12, no. 2 (2013): 82–86. http://dx.doi.org/10.9790/0661-1228286.

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Kaur, Manpreet, and Balwinder Singh. "Classification of printed and handwritten text using hybrid techniques for gurumukhi script." International Journal of Engineering and Computer Science 8, no. 04 (2019): 24586–602. http://dx.doi.org/10.18535/ijecs/v8i04.4298.

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Text classification is a crucial step for optical character recognition. The output of the scanner is non- editable. Though one cannot make any change in scanned text image, if required. Thus, this provides the feed for the theory of optical character recognition. Optical Character Recognition (OCR) is the process of converting scanned images of machine printed or handwritten text into a computer readable format. The process of OCR involves several steps including pre-processing after image acquisition, segmentation, feature extraction, and classification. The incorrect classification is like a garbage in and garbage out. Existing methods focuses only upon the classification of unmixed characters in Arab, English, Latin, Farsi, Bangla, and Devnagari script. The Hybrid Techniques is solving the mixed (Machine printed and handwritten) character classification problem. Classification is carried out on different kind of daily use forms like as self declaration forms, admission forms, verification forms, university forms, certificates, banking forms, dairy forms, Punjab govt forms etc. The proposed technique is capable to classify the handwritten and machine printed text written in Gurumukhi script in mixed text. The proposed technique has been tested on 150 different kinds of forms in Gurumukhi and Roman scripts. The proposed techniques achieve 93% accuracy on mixed character form and 96% accuracy achieves on unmixed character forms. The overall accuracy of the proposed technique is 94.5%.

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Mukherji, Prachi, and Priti P. Rege. "Shape Feature and Fuzzy Logic Based Offline Devnagari Handwritten Optical Character Recognition." Journal of Pattern Recognition Research 5, no. 1 (2010): 52–68. http://dx.doi.org/10.13176/11.48.

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Cecotti, Hubert. "Hierarchical k-Nearest Neighbor with GPUs and a High Performance Cluster: Application to Handwritten Character Recognition." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 02 (2017): 1750005. http://dx.doi.org/10.1142/s0218001417500057.

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The accelerating progress and availability of low cost computers, high speed networks, and software for high performance distributed computing allow us to reconsider computationally expensive techniques in image processing and pattern recognition. We propose a two-level hierarchical [Formula: see text]-nearest neighbor classifier where the first level uses graphics processor units (GPUs) and the second level uses a high performance cluster (HPC). The system is evaluated on the problem of character recognition with nine databases (Arabic digits, Indian digits (Bangla, Devnagari, and Oriya), Bangla characters, Indonesian characters, Arabic characters, Farsi characters and digits). Contrary to many approaches that tune the model for different scripts, the proposed image classification method is unchanged throughout the evaluation on the nine databases. We show that a hierarchical combination of decisions based on two distances, using GPUs and a HPC provides state-of-the-art performances on several scripts, and provides a better accuracy than more complex systems.

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Singh, Brijmohan, Ankush Mittal, and Debashish Ghosh. "Evaluation of Different Feature Extractors and Classifiers for Offline Handwritten Devnagari Character Recognition." Journal of Pattern Recognition Research 6, no. 2 (2011): 269–77. http://dx.doi.org/10.13176/11.302.

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Ritushree, Narayan, and Mishra Puja. "Pattern Recognition of Jharkhand Tribal Language." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 267–71. https://doi.org/10.31142/ijtsrd10854.

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Image processing has wide area for processing various functionality of image. Image with any pattern comes under the categories of pattern recognition where recognizing of the pattern can be any character, symbol, numeral or it can be any image also. Character Recognition CR has broad area of research in Devnagri script. Devnagri script has complicated structure. so, this script has not progressed well. Devanagari character recognition provides less correctness and efficiency. To recognize Devanagari script, various development done which is discuss in detail. Developers used to recognize the pattern with their structure, template, and graph. Some developers use classifiers to segmenting the characters. Ritushree Narayan | Puja Mishra "Pattern Recognition of Jharkhand Tribal Language" 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/ijtsrd10854.pdf

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Firdous, Saniya. "Handwritten Character Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1409–28. http://dx.doi.org/10.22214/ijraset.2022.42114.

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Wadaskar, Ghanshyam, Vipin Bopanwar, Prayojita Urade, Shravani Upganlawar, and Prof Rakhi Shende. "Handwritten Character Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 508–11. http://dx.doi.org/10.22214/ijraset.2023.57366.

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Abstract: Handwritten character recognition is a fascinating topic in the field of artificial intelligence. It involves developing algorithms and models that can analyze and interpret handwritten characters, such as letters, numbers, or symbols. The goal is to accurately convert handwritten text into digital form, making it easier to process and understand. It's a complex task, but with advancements in machine learning and deep learning techniques, significant progress has been made in this area.Handwritten character recognition is all about teaching computers to understand and interpret handwritten text. It involves using advanced algorithms and machine learning techniques to analyze the shapes, lines, and curves of handwritten characters. The goal is to accurately recognize and convert them into digital form. This technology has various applications, such as digitizing handwritten documents, assisting in automatic form filling, and enabling handwriting-based input in devices like tablets and smartphones. It's a fascinating field that combines computer vision, pattern recognition, and artifical intelligence

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Tirapathi Reddy B. "Handwritten Character Recognition System." Journal of Electrical Systems 20, no. 3 (2024): 1465–75. http://dx.doi.org/10.52783/jes.3553.

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Digitizing handwritten documents and enabling efficient information processing and retrieval require systems that can recognize handwritten characters. This research offers a unique approach for handwritten character detection using state-of-the-art machine learning algorithms. The proposed technique automatically extracts discriminative features from photos of handwritten characters using convolutional neural networks (CNNs). These attributes are then used by a classifier to determine which characters are related. The dataset used for training and assessment is made up of a large collection of handwritten characters gathered under various writing styles, sizes, and orientations in order to guarantee the durability and generalization power of the model. To enhance its quality and diversity, the training data is put through a rigorous preparation procedure that includes picture augmentation, noise removal, and normalization. The studies' results demonstrate how well and precisely the proposed system can recognize handwritten characters in a range of languages and writing styles. The system performs competitively compared to state-of-the-art methods and demonstrates robustness against variations in handwriting style and quality. Furthermore, the system has potential in terms of efficiency and scalability, making it suitable for real-time applications such as document digitalization, handwritten word recognition in electronic devices, and automatic form processing.

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Abhale, Poonam Bhanudas. "Handwritten English Alphabet Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 2134–39. http://dx.doi.org/10.22214/ijraset.2021.39703.

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Abstract: Character recognition is a process by which a computer recognizes letters, figures, or symbols and turns them into a digital form that a computer can use. In moment’s terrain character recognition has gained a lot of attention in the field of pattern recognition. Handwritten character recognition is useful in cheque processing in banks, form recycling systems, and numerous further. Character recognition is one of the well- liked and grueling areas of exploration. In the unborn character recognition produce a paperless terrain. In this paper, we describe the detailed study of the being system for handwritten character recognition. We give a literature review on colorful ways used in offline English character recognition. Keywords: Character; Character recognition; Preprocessing; Segmentation; Point birth; Bracket; neural network; Convolution neural network.

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Tiptur Parashivamurthy, Supreetha Patel, and Dr Sannangi Viswaradhya Rajashekararadhya. "An Efficient Kannada Handwritten Character Recognition Framework with Serial Dilated Cascade Network for Kannada Scripts." Advances in Artificial Intelligence and Machine Learning 04, no. 03 (2024): 2499–516. http://dx.doi.org/10.54364/aaiml.2024.43146.

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The most significant problem present in the digitized world is handwritten character recognition and identification because it is helpful in various applications. The manual work needed for changing the handwritten character document into machine-readable texts is highly reduced by using the automatic identification approaches. Due to the factors of high variance in the writing styles beyond the globe, handwritten text size and low quality of handwritten text rather than printed text make handwritten character recognition to be very complex. The Kannada language has originated over the past 1000 years, where the consonants and vowels are symmetric in nature and also curvy, therefore, the recognition of Kannada characters online is very difficult. Thus, it is essential to overcome the above-mentioned complications presented in the classical Kannada handwritten character recognition model. The recognition of characters from Kannada Scripts is also difficult. Hence, this work aims to design a new Kannada handwritten character recognition framework using deep learning techniques from Kannada scripts. There are two steps to be followed in the proposed model that is collection of images and classification of handwritten characters. At first, essential handwritten Kannada characters are collected from the benchmark resources. Next, the acquired handwritten Kannada images are offered to the handwritten Kannada character recognition phase. Here, Kannada character recognition is performed using Serial Dilated Cascade Network (SDCN), which utilized the Visual Geometry Group 16 (VGG16) and Deep Temporal Convolution Network (DTCN) technique for the observation. When compared to the baseline recognition works, the proposed handwritten Kannada character recognition model achieves a significantly higher performance rate.

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Naik, Dr Vishal, and Heli Mehta. "Comparison of Various Algorithms for Handwritten Character Recognition of Indian Languages." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 696–703. http://dx.doi.org/10.22214/ijraset.2023.56079.

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Abstract: In this paper, we present a comparison of various pre-processor, feature extraction methods and algorithms for handwritten character recognition of various Indian languages. Comparison of classifier, feature set and accuracy of offline handwritten character recognition of Gujarati, Devanagari, Gurmukhi, Kannada, Malayalam, Bangla and Hindi Indian languages. Comparison of classifier, feature set and accuracy of online handwritten character recognition of Assamese, Tamil, Devanagari, Malayalam, Gurmukhi, and Bangla Indian languages. Indian language wise best performance of each language is compared for both offline and online handwritten character recognition systems.

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Ning, Zihao. "Research on Handwritten Chinese Character Recognition Based on BP Neural Network." Modern Electronic Technology 6, no. 1 (2022): 12. http://dx.doi.org/10.26549/met.v6i1.11359.

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The application of pattern recognition technology enables us to solve various human-computer interaction problems that were difficult to solve before. Handwritten Chinese character recognition, as a hot research object in image pattern recognition, has many applications in people’s daily life, and more and more scholars are beginning to study off-line handwritten Chinese character recognition. This paper mainly studies the recognition of handwritten Chinese characters by BP (Back Propagation) neural network. Establish a handwritten Chinese character recognition model based on BP neural network, and then verify the accuracy and feasibility of the neural network through GUI (Graphical User Interface) model established by Matlab. This paper mainly includes the following aspects: Firstly, the preprocessing process of handwritten Chinese character recognition in this paper is analyzed. Among them, image preprocessing mainly includes six processes: graying, binarization, smoothing and denoising, character segmentation, histogram equalization and normalization. Secondly, through the comparative selection of feature extraction methods for handwritten Chinese characters, and through the comparative analysis of the results of three different feature extraction methods, the most suitable feature extraction method for this paper is found. Finally, it is the application of BP neural network in handwritten Chinese character recognition. The establishment, training process and parameter selection of BP neural network are described in detail. The simulation software platform chosen in this paper is Matlab, and the sample images are used to train BP neural network to verify the feasibility of Chinese character recognition. Design the GUI interface of human-computer interaction based on Matlab, show the process and results of handwritten Chinese character recognition, and analyze the experimental results.

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Somashekar, Thatikonda. "A Survey on Handwritten Character Recognition using Machine Learning Technique." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 1019–24. http://dx.doi.org/10.51201/jusst/21/05304.

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Due to its broad range of applications, handwritten character recognition is widespread. Processing application forms, digitizing ancient articles, processing postal addresses, processing bank checks, and many other handwritten character processing fields are increasing in popularity. Since the last three decades, handwritten characters have drawn the attention of researchers. For successful recognition, several methods have been suggested. This paper presents a comprehensive overview of handwritten character recognition using a neural network as a machine learning tool.

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Wakahara, Toru. "Toward robust handwritten character recognition." Pattern Recognition Letters 14, no. 4 (1993): 345–54. http://dx.doi.org/10.1016/0167-8655(93)90100-r.

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Soselia, Davit, Magda Tsintsadze, Levan Shugliashvili, Irakli Koberidze, Shota Amashukeli, and Sandro Jijavadze. "On Georgian Handwritten Character Recognition." IFAC-PapersOnLine 51, no. 30 (2018): 161–65. http://dx.doi.org/10.1016/j.ifacol.2018.11.279.

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Santosh, Acharya, Dhungel Shashank, and Kr. Jha Ashish. "Nepali Handwritten Character Recognition System." Advancement in Image Processing and Pattern Recognition 5, no. 3 (2022): 1–6. https://doi.org/10.5281/zenodo.7472398.

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Even if the technological and digital world is expanding more quickly, there are still many things that are lacking. What a wonderful thing it would be to be able to trust machines to scan any handwritten characters into digital representation. The method for doing this is called optical character recognition (OCR), but there is still much room for improvement. Although there has been work done on it, the technique developed for one language cannot be applied to another due to language variations. Nepali is not a language that is frequently used online. Perhaps this is why there are fewer OCR systems developed using this language. We have made an effort to improve on it so that Nepali characters can be recognized. Basically, the idea is to use a camera to scan Nepali handwriting from hard copy paper, locate the regions in the image where the characters are present, segment those localized parts into characters, and then digitally display each predicted segmented character.

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Shrivastav, Jitendra, Ravindra Kumar Gupta, and Shailendra Singh. "A Modified Back propagation Algorithm for Optical Character Recognition." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 06 (2013): 180–84. https://doi.org/10.5281/zenodo.14605792.

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Character Recognition (CR) has been an active area of research and due to its diverse applicable environment; it continues to be a challenging research topic. There is a clear need for optical character recognition in order to provide a fast and accurate method to search both existing images as well as large archives of existing paper documents. However, existing optical character recognition programs suffer from a flawed tradeoff between speed and accuracy, making it less attractive for large quantities of documents. In this thesis, we present a new neural network based method for optical character recognition as well as handwritten character recognition. Experimental results show that our proposed method achieves highest percent accuracy in optical character recognition. We present an overview of existing handwritten character recognition techniques. All these algorithms are described more or less on their own. Handwritten character recognition is a very popular and computationally expensive task. We also explain the fundamentals of handwritten character recognition. We describe today’s approaches for handwritten character recognition. From the broad variety of efficient techniques that have been developed we will compare the most important ones. We will systematize the techniques and analyze their performance based on both their run time performance and theoretical considerations. Their strengths and weaknesses are also investigated. It turns out that the behavior of the algorithms is much more similar as to be expected. 

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Haithem Abd Al-RaheemTaha. "ON-LINE HANDWRITTEN ARABIC CHARACTER RECOGNITION BASED ON GENETIC ALGORITHM." Diyala Journal of Engineering Sciences 5, no. 1 (2012): 79–87. http://dx.doi.org/10.24237/djes.2012.05107.

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On-line Arabic handwritten character recognition is one of the most challenging problems in pattern recognition field. By now, printed Arabic character recognition and on-line Arabic handwritten recognition has been gradually practical, while offline Arabic handwritten character recognition is still considered as "The hardest problem to conquer" in this field due to its own complexity. Recently, it becomes a hot topic with the release of database, which is the first text-level database and is concerned about the area of realistic Arabic handwritten character recognition. At the realistic Arabic handwritten text recognition and explore two aspects of the problem. Firstly, a system based on segmentation-recognition integrated framework was developed for Arabic handwriting recognition. Secondly, the parameters of embedded classifier initialed at character-level training were discriminatively re-trained at string level. The segmentation-recognition integrated framework runs as follows: the written character is first over-segmented into primitive segments, and then the consecutive segments are combined into candidate patterns. The embedded classifier is used to classify all the candidate patterns in segmentation lattice. According to Genetic Algorithm (Crossover, mutation, and population), the system outputs the optimal path in segmentation-recognition lattice, which is the final recognition result. The embedded classifier is first trained at character level on isolated character and then the parameters are updated at string level on string samples.

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N D, Sukesh, and Steephan Amalraj J. "Handwritten Character Recognition Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25945.

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Handwritten digit or character recognition in transforming the printed or handwritten text from an image. Optical character recognition plays an important role in documentation scanning ,text extractions from the image. Optical character recognition is used in different fields like postal services ,Ecommerce , Shipping ,Banking sector for character extraction from the images . However the existing character recognition system faces many challenges in extracting text from noisy and distortion images or complex layout and Extraction mostly limited to numbers and English alphabets . The introduction of Deep learning has changed Optical Character Recognition by using models like Recurrent Neural Networks,convolutional neural network .In this paper i am gonna compare the different models like CNN model and CRNN model with current State of art model Transformer based Optical Character Recognition KeyWords :Transformers ,Convolution Recurrent Neural Network, Handwritten ,Optical Character Recognition

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Alharbi, Abir. "A Genetic-LVQ neural networks approach for handwritten Arabic character recognition." Artificial Intelligence Research 7, no. 2 (2018): 43. http://dx.doi.org/10.5430/air.v7n2p43.

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Handwritten recognition systems are a dynamic field of research in areas of artificial intelligence. Many smart devices available in the market such as pen-based computers, tablets, mobiles with handwritten recognition technology need to rely on efficient handwritten recognition systems. In this paper we present a novel Arabic character handwritten recognition system based on a hybrid method consisting of a genetic algorithm and a Learning vector quantization (LVQ) neural network. Sixty different handwritten Arabic character datasets are used for training the neural network. Each character dataset contains 28 letters written twice with 15 distinct shaped alphabets, and each handwritten Arabic letter is represented by a binary matrix that is used as an input to a genetic algorithm for feature selection and dimension reduction to include only the most effective features to be fed to the LVQ classifier. The recognition process in the system involves several essential steps such as: handwritten letter acquisition, dataset preparation, feature selection, training, and recognition. Comparing our results to those acquired by the whole feature dataset without selection, and to the results using other classification algorithms confirms the effectiveness of our proposed handwritten recognition system with an accuracy of 95.4%, hence, showing a promising potential for improving future handwritten Arabic recognition devices in the market.

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Riasat, Azim, Fazlul Karim M., and Rahman Wahidur. "Bangla Hand Written Character Recognition Using Support Vector Machine." International Journal of Engineering Works (ISSN: 2409-2770) 3, no. 6 (2016): 36–46. https://doi.org/10.5281/zenodo.60329.

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Recognizing handwritten character using computer is still consider a strong area of research. A fundamental problem in the field of Bangla character recognition is the lack of availability of Bangla handwritten character data set. In this thesis our main objective is to generate a larger dataset of Bangla character and as well as improving the recognition rate using Support Vector Machine. Support Vector Machines (SVM) is used for classification in pattern recognition widely. In our proposed method we applied support vector machine for increasing the recognition rate. A scanner is used to capture handwritten data sheet written in white paper by various people. After that several approaches used to generate the final data set for training and testing in SVM. A cropped image is scaled into 16*16 pixel matrix and then combing large number of image a dataset is produced. A binary classification technique of Support Vector Machine is implemented and rbf kernel function is used in SVM. This rbf SVM produces 93.43% overall recognition rate which is satisfactory result among all techniques applied on handwritten Bangla handwritten character recognition system.

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Jehangir, Sardar, Sohail Khan, Sulaiman Khan, Shah Nazir, and Anwar Hussain. "Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis." January 2021 40, no. 1 (2021): 152–59. http://dx.doi.org/10.22581/muet1982.2101.14.

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This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten Pashto characters database. For experimental and simulation purposes a handwritten Pashto characters database is developed by collecting handwritten samples from the students of the university on A4 sized page. These collected samples are then scanned, stemmed and preprocessed to form a medium sized database that encompasses 14784 handwritten Pashto character images (336 distinguishing handwritten samples for each 44 characters in Pashto script). Furthermore, the Zernike moments are considered as a feature extractor tool for the proposed OCR system to extract features of each individual character. Linear Discriminant Analysis (LDA) is followed as a recognition tool for the proposed recognition system based on the calculated features map using Zernike moments. Applicability of the proposed system is tested by validating it with 10-fold cross-validation method and an overall accuracy of 63.71% is obtained for the handwritten Pashto isolated characters using the proposed OCR system.

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Sameer, Bawaneh. "Handwritten Recognition (numbers)." European Journal of Information technology and Project Management 1, no. 2 (2019): 7–12. https://doi.org/10.5281/zenodo.3229015.

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due to the magnitude of the neural network science and MATLAB in terms of tools and algorithms, we will present a simple algorithm and explain it in general in this project, on the other hand due to we do not have enough knowledge in this field. In this project we will provide an overview of the role of handwriting recognition in neural network by using Optical Character Recognition algorithm.

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BATUWITA, RUKSHAN, VASILE PALADE, and DHARMAPRIYA C. BANDARA. "A CUSTOMIZABLE FUZZY SYSTEM FOR OFFLINE HANDWRITTEN CHARACTER RECOGNITION." International Journal on Artificial Intelligence Tools 20, no. 03 (2011): 425–55. http://dx.doi.org/10.1142/s021821301100022x.

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Automated offline handwritten character recognition involves the development of computational methods that can generate descriptions of the handwritten objects from scanned digital images. This is a challenging computational task, due to the vast impreciseness associated with the handwritten patterns of different individuals. Therefore, to be successful, any solution should employ techniques that can effectively handle this imprecise knowledge. Fuzzy Logic, with its ability to deal with the impreciseness arisen due to lack of knowledge, could be successfully used to develop automated systems for handwritten character recognition. This paper presents an approach towards the development of a customizable fuzzy system for offline handwritten character recognition.

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Manoj, Sonkusare, and Sahu Narendra. "A SURVEY ON HANDWRITTEN CHARACTER RECOGNITION (HCR) TECHNIQUES FOR ENGLISH ALPHABETS." Advances in Vision Computing: An International Journal (AVC) 3, no. 1 (2016): 01–12. https://doi.org/10.5281/zenodo.3461522.

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Nowadays Hand written Character Recognition (HCR) is major remarkable and difficult research domain in the area of Image processing. Recognition of Handwritten English alphabets have been broadly studied in the previous years. Presently various recognition methodologies are in well-known utilized for recognition of handwritten English alphabets (character). Application domain of HCR is digital document processing such as mining information from data entry, cheque, applications for loans, credit cards, tax, health insurance forms etc. During this survey we present an outline of current research work conducted for recognition of handwritten English alphabets. In Handwritten manuscript there is no restriction on the writing technique. Handwritten alphabets are complicated to recognize because of miscellaneous human handwriting technique, difference in size and shape of letters, angle. A variety of recognition methodologies for handwritten English alphabets are conferred here alongside with their performance.

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Manoj, Sonkusare, and Sahu Narendra. "A SURVEY ON HANDWRITTEN CHARACTER RECOGNITION (HCR) TECHNIQUES FOR ENGLISH ALPHABETS." Advances in Vision Computing: An International Journal (AVC) 3, no. 1 (2016): 01–12. https://doi.org/10.5281/zenodo.3626432.

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Nowadays Hand written Character Recognition (HCR) is major remarkable and difficult research domain in the area of Image processing. Recognition of Handwritten English alphabets have been broadly studied in the previous years. Presently various recognition methodologies are in well-known utilized for recognition of handwritten English alphabets (character). Application domain of HCR is digital document processing such as mining information from data entry, cheque, applications for loans, credit cards, tax, health insurance forms etc. During this survey we present an outline of current research work conducted for recognition of handwritten English alphabets. In Handwritten manuscript there is no restriction on the writing technique. Handwritten alphabets are complicated to recognize because of miscellaneous human handwriting technique, difference in size and shape of letters, angle. A variety of recognition methodologies for handwritten English alphabets are conferred here alongside with their performance. 

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Asghar, Ali Chandio, Leghari Mehwish, Orangzeb Panhwar Ali, Zaman Nizamani Shah, and Leghari Mehjabeen. "Deep learning-based isolated handwritten Sindhi character recognition." Indian Journal of Science and Technology 13, no. 25 (2020): 2565–74. https://doi.org/10.17485/IJST/v13i25.914.

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Abstract Motivation : The problem of handwritten text recognition is vastly studied since last few decades. Many innovative ideas have been developed, where state-of-the-art accuracy is achieved for the English, Chinese or Indian scripts.The recent developments for the cursive scripts such as Arabic and Urdu handwritten text recognition have achieved remarkable accuracy. However, for the Sindhi script, existing systems have not shown significant results and the problem is still an open challenge. Several challenges such as variations in writing styles, joined text, ligature overlapping, and others associated to the handwritten Sindhi text make the problem more complex. Objectives: In this study, a deep residual network with shortcut connections and summation fusion method using convolutional neural network (CNN) is proposed for automatic feature extraction and classification of handwritten Sindhi characters. Method: To increase the powerful feature representation ability of the network, the features of the convolutional layers in the residual block are fused together and combined with the output of the previous residual block. The proposed network is trained on a custom developed handwritten Sindhi character dataset. To tackle the problem of small data, a data augmentation with rotation, flipping and image enhancement techniques have been used. Findings: The experimental results show that the proposed model outperforms than the best results previously published for the handwritten Sindhi character recognition. Novelty: This is the first research that proposes deep residual network with summation fusion for the Sindhi handwritten text recognition. Keywords: Handwritten Sindhi character recognition; Sindhi text recognition; cursive text recognition; deep learning; ResNet; convolutional neural network

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Liu, Hanqi. "Handwritten English character recognition using convolutional neural network." Applied and Computational Engineering 4, no. 1 (2023): 199–204. http://dx.doi.org/10.54254/2755-2721/4/20230450.

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Converting paper material into electronic material is still a necessary work nowadays, however, recognition of handwritten characters still has limitations in their recognition rate, owing to the presence of various shapes, scales, and formats in different peoples handwritten characters. Machine learning has significant value in reducing human power. A Convolutional Neural Network model that is revised from LeNet-5, is used for handwritten letter recognition. This study uses the EMINST dataset to train the model, and the final recognition rate is about 93.44%.

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Teja, K. Sai. "Hindi-Handwritten-Character- Recognition using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 369–73. http://dx.doi.org/10.22214/ijraset.2023.54606.

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Abstract: Hindi-Handwritten-Character- Recognition is animportant problem in the field of machine learning andcomputer vision. With the increasing digitization of India, there is a growing need to develop accurate and efficient algorithms for recognizing handwritten Hindi characters, which can be used in a variety of applications such as document analysis, postal automation, and data entry. In recent years, deep learning has emerged as a powerful tool for solving complex recognition problems. In this work, we propose a deep learning-based approach to the Hindi-Handwritten Character-Recognition. Specifically, we use a convolutional neural network (CNN) to extract features from the input images, and are current neural network (RNN) to model the temporal dependencies in the sequence of characters. Our approach is evaluated on a benchmark dataset of handwritten Hindi characters, achieving state-of- the-art results in terms of recognition accuracy. We also demonstrate the effectiveness of our approach on real-worldapplications, such as recognizing handwritten postal addresses on envelopes. Overall, our work provides a promising solution to the problem of Hindi-Hand-written- Character-Recognition, which can havea significant impact on the digitization of India and other similar regions.

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Ishan, Gulati*1 Gautam Vig2 &. Vijay Khare3. "REAL TIME HANDWRITTEN CHARACTER RECOGNITION USING ANN." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 357–62. https://doi.org/10.5281/zenodo.1218609.

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-Real time  Handwritten Character Recognition by using Template Matching is a system which is useful to recognize the character or alphabets in the given text by comparing two images of the alphabet. The objectives of this system prototype are to develop a program for the Optical Character Recognition (OCR) system by using the Template Matching algorithm . Handwritten character recognition is a challenging task in the field of research on image processing, artificial intelligence as well as machine vision since the handwriting varies from person to person. Moreover, the handwriting styles, sizes and its orientation make it even more complex to interpret the text. The numerous applications of handwritten text in reading bank cheques, Zip Code recognition and in removing the problem of handling documents manually has made it necessary to acquire digitally formatted data. This paper presents the recognition of handwritten characters using either a scanned document, or direct acquisition of image using Matlab, followed by the implementation of various other Matlab toolboxes like Image Processing and Neural Network Toolbox to process the scanned or acquired image. Experimental Results are given to present the proposed model in order to recognize handwritten characters accurately.

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Mujadded, Al Rabbani Alif. "State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture." State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture 9, no. 1 (2024): 11. https://doi.org/10.5281/zenodo.10538255.

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Bangla Handwritten Character Recognition (HCR) remains a persistent challenge within the domain of Optical Character Recognition (OCR) systems. Despite extensive research efforts spanning several decades, achieving satisfactory success in this field has proven to be complicated. Bangla, being one of the most widely spoken languages worldwide, consists of 50 primary characters, including 11 vowels and 39 consonants. Unlike Latin languages, Bangla characters exhibit complex patterns, diverse sizes, significant variations, intricate letter shapes, and intricate edges. These characteristics further differ based on factors such as the writer's age and birthplace. In this paper, we propose a modified ResNet-34 architecture, a convolutional neural network (CNN) model, to identify Bangla handwritten characters accurately. The proposed approach is validated using a merged subset of two popular Bangla handwritten  datasets. Through our technique, we achieve state-of-the- art recognition performance. Experimental results  demonstrate that the suggested model attains an average accuracy of 98.70% for Bangla handwritten vowels, 97.34% for consonants, and 99.02% for numeric characters. Additionally, when applied to a mixed dataset comprising vowels, consonants, and numeric characters, the proposed model achieves an overall accuracy of 97%. This research contributes to advancing digital manufacturing systems by addressing the challenge of Bangla Handwritten Character Recognition, offering a high-performing solution based on a modified ResNet-34 architecture. The achieved recognition accuracy signifies significant progress in this field, potentially paving the way for enhanced automation and efficiency in various applications that involve processing Bangla handwritten text. Keywords:- Handwritten Character Recognition; ResNet; Optical Character Recognition; Computer Vision; Convolutional Neural Networks.

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N S, Aswin. "Malayalam Handwritten Words Recognition: A Review." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30057.

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This review examines character segmentation and offers an elegant method for identifying and transforming handwritten Malayalam words from picture documents into text. Character touchings, different writing styles, and noisy, damaged scanned photos make it difficult to recognise handwritten text. Taking use of today's world of rich data and algorithmic developments, the system uses deep convolutional neural networks (CNNs) to address these challenges. The three steps of Malayalam handwritten word recognition are segmentation, recognition, and pre-processing. Making Malayalam character datasets is the first stage, and then pre-processing to improve image quality comes next. Then, in order to maximise the system's capacity to precisely forecast Malayalam characters, a CNN model is built to extract relevant information. The last phase of the recognition process involves the system classifying the characters. This project is significant since it uses CNN filters to enhance feature recognition, which enhances the accuracy of Malayalam character prediction. Key Words: Deep Learning, Deep Convolution Neural Network (DCNN), Character recognition, Character segmentation,

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Bhagat, Ms Shubhangee S. "Handwritten Character Detection Using Optical Character Recognition Method." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 4724–26. http://dx.doi.org/10.22214/ijraset.2018.4775.

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Amulya, K., Lakshmi Reddy, M. Chandara Kumar, and Rachana D. "A Survey on Digitization of Handwritten Notes in Kannada." International Journal of Innovative Technology and Exploring Engineering 12, no. 1 (2022): 6–11. http://dx.doi.org/10.35940/ijitee.a9350.1212122.

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Recognition of handwritten text is still an unresolved research problem in the field of optical character recognition. This article suggests an efficient method for creating handwritten text recognition systems. This is a challenging subject that has received a lot of attention recently. A discipline known as optical character recognition makes it possible to convert many kinds of texts or photos into editable, searchable, and analyzable data. Researchers have been using artificial intelligence and machine learning methods to automatically evaluate printed and handwritten documents during the past ten years in order to digitize them. This review paper's goals are to present research directions and a summary of previous studies on character recognition in handwritten texts. Since different people have different handwriting styles, handwritten characters might be challenging to read. Our "Digitization of handwritten notes" research and effort is to categorize and identify characters in the south Indian language of Kannada. The characters are extracted from printed texts and pre-processed using NumPy and OpenCV before being fed through a CNN

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K, Amulya, Reddy Lakshmi, Chandara Kumar M, and D. Rachana. "A Survey on Digitization of Handwritten Notes in Kannada." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 12, no. 1 (2022): 6–11. https://doi.org/10.35940/ijitee.A9350.1212122.

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Abstract: Recognition of handwritten text is still an unresolved research problem in the field of optical character recognition. This article suggests an efficient method for creating handwritten text recognition systems. This is a challenging subject that has received a lot of attention recently. A discipline known as optical character recognition makes it possible to convert many kinds of texts or photos into editable, searchable, and analyzable data. Researchers have been using artificial intelligence and machine learning methods to automatically evaluate printed and handwritten documents during the past ten years in order to digitize them. This review paper's goals are to present research directions and a summary of previous studies on character recognition in handwritten texts. Since different people have different handwriting styles, handwritten characters might be challenging to read. Our "Digitization of handwritten notes" research and effort is to categorize and identify characters in the south Indian language of Kannada. The characters are extracted from printed texts and pre-processed using NumPy and OpenCV before being fed through a CNN.

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Kumar, K. Sathish, M. Shashivardhan Reddy, D. Hemanth Kumar, D. Shiva Kumar, N. Shiva, and Dr D. Thiyagarajan. "Hindi-Handwritten-Character-Recognition using Deep learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5252–55. http://dx.doi.org/10.22214/ijraset.2024.62768.

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Abstract: Recognizing handwritten Hindi characters poses a significant challenge in the realms of machine learning and computer vision, particularly in the context of India's accelerating digitization. To address this, accurate and efficient algorithms are imperative for applications ranging from document analysis to postal automation and data entry. Leveraging the advancements in deep learning, we propose a novel approach to Hindi Handwritten Character Recognition. Our method employs a combination of Convolutional Neural Networks (CNNs) to extract image features and Recurrent Neural Networks (RNNs) to capture temporal dependencies within character sequences. Through rigorous evaluation on a standard benchmark dataset, our approach achieves state-of-the-art recognition accuracy. Furthermore, we validate its practical utility by successfully recognizing handwritten postal addresses on envelopes and other real-world applications. This research offers a promising solution to the challenges of Hindi Handwritten Character Recognition, with potential implications for advancing the digitization efforts not only in India but also in analogous regions.

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Mehta, Nikita, and Jyotika Doshi. "A Review of Handwritten Character Recognition." International Journal of Computer Applications 165, no. 4 (2017): 37–40. http://dx.doi.org/10.5120/ijca2017913855.

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Sahu, Manish Kumar, and Dr Naveen Kumar Dewangan. "A Survey on Handwritten Character Recognition." IARJSET 4, no. 1 (2017): 89–91. http://dx.doi.org/10.17148/iarjset.2017.4120.

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Sahu, Manish Kumar, and Naveen Kumar Dewangan. "Handwritten Character Recognition using Neural Network." IJARCCE 6, no. 6 (2017): 11–14. http://dx.doi.org/10.17148/ijarcce.2017.6603.

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Sinha, Gita, Dr Shailja Sharma, and Rakesh Kumar Roshan. "CLASSIFICATION TECHNIQUES FOR HANDWRITTEN CHARACTER RECOGNITION." International Journal of Engineering Applied Sciences and Technology 5, no. 3 (2020): 151–57. http://dx.doi.org/10.33564/ijeast.2020.v05i03.023.

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Li, Ling Hua, Shou Fang Mi, and Heng Bo Zhang. "Template-Based Handwritten Numeric Character Recognition." Advanced Materials Research 586 (November 2012): 384–88. http://dx.doi.org/10.4028/www.scientific.net/amr.586.384.

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This paper describes a stroke-based handwriting analysis method in classifying handwritten Numeric characters by using a template-based approach. Writing strokes are variable from time to time, even when the writing character is same and comes from the same user. Writing strokes include the properties such as the number of the strokes, the shapes and sizes of them and the writing order and the writing speed. We describe here a template-based system using the properties of writing strokes for the recognition of online handwritten numeric characters. Experimental results show that within the 1500 numeric characters taken from 30 writers, the system got 97.84% recognition accuracy which is better than other systems shown by other literatures.

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Wang, Xian, Venu Govindaraju, and Sargur Srihari. "Holistic recognition of handwritten character pairs." Pattern Recognition 33, no. 12 (2000): 1967–73. http://dx.doi.org/10.1016/s0031-3203(99)00204-6.

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Wakahara, Toru, and Yoshimasa Kimura. "Toward robust handwritten Kanji character recognition." Pattern Recognition Letters 20, no. 10 (1999): 979–90. http://dx.doi.org/10.1016/s0167-8655(99)00065-3.

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Bourbakis, N. G., C. Koutsougeras, and A. Jameel. "Handwritten character recognition using low resolutions." Engineering Applications of Artificial Intelligence 12, no. 2 (1999): 139–47. http://dx.doi.org/10.1016/s0952-1976(98)00062-1.

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Journal articles on the topic 'Handwritten Devnagri character recognition'

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