Relevant bibliographies by topics / Handwritten character recognition system (HCR)

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

Academic literature on the topic 'Handwritten character recognition system (HCR)'

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

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Journal articles on the topic "Handwritten character recognition system (HCR)"

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BV Subba Rao, Et al. "Unicode-driven Deep Learning Handwritten Telugu-to-English Character Recognition and Translation System." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 344–59. http://dx.doi.org/10.17762/ijritcc.v11i10.8497.

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Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.
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Rasika, R. Janrao *. Mr. D. D. Dighe. "HANDWRITTEN ENGLISH CHARACTER RECOGNITION USING LVQ AND KNN." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 8 (2016): 904–12. https://doi.org/10.5281/zenodo.60830.

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A Handwritten character recognition (HCR) is an important task of detecting and recognizing in characters from the input digital image and convert it to other equivalent machine editable form. It gives high growth in image processing and pattern recognition. It has big challenges in data interpretation from language identification, bank cheques and conversion of any handwritten document into structural text form. Handwritten character recognition system uses a soft computing method like neural network, having area of research for long time with multiple theories and developed algorithm. Feature Extraction done in character recognition by introducing a new approach, diagonal based feature extraction. We used two Dataset, first one is own database of 26 alphabets, 10 numbers and 5 special characters written by various people and second is standard CEDAR database. The character recognition is carried out by supervised KNN classifier and LVQ. The results show that KNN has better results than LVQ. 
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Shital, Bagal, Deore Vaishnavi, Adsare Samiksha, Thube Shreya, and Bhandakkar M.P. "HAND WRITTEN CHARACTER RECOGNITION USING DEEP NEURAL NETWORK." Journal of the Maharaja Sayajirao University of Baroda 59, no. 1 (I) (2025): 233–37. https://doi.org/10.5281/zenodo.15180414.

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ABSTRACTHandwritten character recognition (HCR) is a challenging problem in the field of computer vision,with numerous applications in automated data entry, postal sorting, and document digitization. Thispaper presents an approach for handwritten character recognition using deep neural networks,specifically employing a Convolutional Neural Network (CNN) algorithm. The CNN model is trainedon a comprehensive dataset of handwritten characters, and it automatically learns and extracts relevantfeatures from the input images. Through various layers of convolution, pooling, and fully connectednetworks, the system achieves high accuracy in recognizing characters by capturing intricate detailssuch as stroke patterns and shapes. The proposed method significantly improves recognition accuracycompared to traditional machine learning approaches by utilizing CNN's capacity for feature extractionand pattern recognition. The model's performance is validated through experiments on standardbenchmark datasets, showing superior results in terms of precision, recall, and F1 score. Key Words: Handwritten Character Recognition, Convolutional Neural Network, Deep NeuralNetworks, Image Processing, Pattern Recognition, Feature Extraction, Machine Learning.
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VINITA, PATIL. "Modeling of Hybrid CNN Framework for Handwritten Character and Digit Recognition System." Advanced Engineering Science 54, no. 2 (2023): 661–73. https://doi.org/10.5281/zenodo.7890787.

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Purpose: In this paper highlights the recognition of hand-written Content/character problems that have been proposed. This study shows the emerging and widely used in many vision-based applications in recent years. In this work, the drawback of conventional handwriting recognition utilizes the statistical characteristics of individual Content/characters, and different subsets to recognize the different Content/characters are overcome. Design/Methodology/Approach: To mitigate this problem several works have been motivated to incorporate a deep learning model for handwriting recognition and achieved significant performance improvement in recent times. In deep learning models, Convolutional neural networks (CNNs) are providing promising solutions to automate the distinct feature extraction from all given training samples. In this work pre-processing measures have been used to explore the boundary details of input samples for CNN-based handwritten character and digit recognition systems. Here, we proposed a hybrid novel CNN model which combines the detailed feature extraction for final classification. The proposed CNN framework is trained using the Modified National Institute of Standards and Technology (MNIST) database. Findings/Result: The performance validation includes both character and digits classification from given benchmark image sets. The validation also includes some self-build handwritten characters and digits for analyzing its resistance to the real-time capture. From the experimental results, it is observed that the variation inaccuracies are significant with the number of layers used in the CNN network. Finally, to validate the performance metrics of the proposed handwritten character recognition system it is compared with the existing state-of-the-art HCR models in terms of overall recognition accuracy. Originality/Value: In this work, the drawback of conventional handwriting recognition utilizes the statistical characteristics of individual Content/characters, and different subsets to recognize the different Content/characters are overcome. The objectives for accommodating handwritten content/characters written in different formats are difficult to accomplish irrespective of the methodologies used for classification.
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Gassi, Sajad Ahmad, Ravinder Pal Singh, and Dr Monika Mehra. "Real Time Character Recognition using Convolution Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1156–62. http://dx.doi.org/10.22214/ijraset.2022.47540.

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Abstract: Handwritten recognition of character (HCR) is a significant element in the current world and one of the focused fields in image processing and pattern recognition research. Handwritten recognition of character refers to the process of converting hand-written character into printed/word file character that in many applications may greatly enhance the interaction of man and machine. The styles, varied sizes and orientation angles of the current characters are tough to parse with large variances. In addition, it is hard to split cursive handwritten text as the edges cannot be clearly seen. Many ways of recognizing handwritten data are available. The proposed research is based on 5*5 convolution neural network where the performance of the system has been enhanced in terms of accuracy, precision and recall the data set. The research utilized the real time photos. The processing approaches are followed by binarization, skeletonisation, dilution, resizing, segmentation and extraction. The character characteristics are sent to CNN to train the models after preprocessing. The research achieved 92% accuracy and time delay while detecting the real time Images.
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Hamdan, Yasir Babiker, and Sathish. "Construction of Statistical SVM based Recognition Model for Handwritten Character Recognition." June 2021 3, no. 2 (2021): 92–107. http://dx.doi.org/10.36548/jitdw.2021.2.003.

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There are many applications of the handwritten character recognition (HCR) approach still exist. Reading postal addresses in various states contains different languages in any union government like India. Bank check amounts and signature verification is one of the important application of HCR in the automatic banking system in all developed countries. The optical character recognition of the documents is comparing with handwriting documents by a human. This OCR is used for translation purposes of characters from various types of files such as image, word document files. The main aim of this research article is to provide the solution for various handwriting recognition approaches such as touch input from the mobile screen and picture file. The recognition approaches performing with various methods that we have chosen in artificial neural networks and statistical methods so on and to address nonlinearly divisible issues. This research article consisting of various approaches to compare and recognize the handwriting characters from the image documents. Besides, the research paper is comparing statistical approach support vector machine (SVM) classifiers network method with statistical, template matching, structural pattern recognition, and graphical methods. It has proved Statistical SVM for OCR system performance that is providing a good result that is configured with machine learning approach. The recognition rate is higher than other methods mentioned in this research article. The proposed model has tested on a training section that contained various stylish letters and digits to learn with a higher accuracy level. We obtained test results of 91% of accuracy to recognize the characters from documents. Finally, we have discussed several future tasks of this research further.
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Neha*1, &. Deepti Ahlawat2. "HANDWRITTEN ALPHANUMERIC CHARACTER RECOGNITION AND COMPARISON OF CLASSIFICATION TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 1 (2018): 419–28. https://doi.org/10.5281/zenodo.1147604.

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Several techniques have been proposed by many researchers for handwritten as well as printed character and numerals recognition. Recognition is the process of conversion of handwritten text into machine readable form. To achieve the best accuracy of any recognition system the selection of feature extraction and classification technique is important. The data about the character is collected by the features and accordingly classifiers classify the character uniquely. For handwritten characters there are drawbacks like it differs from one writer to another, even when same person writes same character a number of times there is difference in shape, size and position of character. Latest research in this area have used various types of method, classifiers and features to reduce complexity of recognizing handwritten text. In this paper, advantages and disadvantages of two different techniques of feature extraction and classification have been discussed.
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Li, Lei, Xue Gao, and Lianwen Jin. "HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm." Scientific Programming 2018 (September 5, 2018): 1–16. http://dx.doi.org/10.1155/2018/6509275.

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Handwritten character recognition (HCR) is a mainstream mobile device input method that has attracted significant research interest. Although previous studies have delivered reasonable recognition accuracy, it remains difficult to directly embed the advanced HCR service into mobile device software and obtain excellent but fast results. Cloud computing is a relatively new online computational resource provider which can satisfy the elastic resource requirements of the advanced HCR service with high-recognition accuracy. However, owing to the delay sensitivity of the character recognition service, the performance loss in the traditional cloud virtualization technology (e.g., kernel-based virtual machine (KVM)) may impair the performance. In addition, the improper computational resource scheduling in cloud computing impairs not only the performance but also the resource utilization. Thus, the HCR online service is required to guarantee the performance and improve the resource utilization of the HCR service in cloud computing. To address these problems, in this paper, we propose an HCR container as a service (HCRCaaS) in cloud computing. We address several key contributions: (1) designing an HCR engine on the basis of deep convolution neutral networks as a demo for an advanced HCR engine with better recognition accuracy, (2) providing an isolated lightweight runtime environment for high performance and easy expansion, and (3) designing a greedy resource scheduling algorithm based on the performance evaluation to optimize the resource utilization under a quality of service (QoS) guaranteeing. Experimental results show that our system not only reduces the performance loss compared with traditional cloud computing under the advanced HCR algorithm but also improves the resource utilization appropriately under the QoS guaranteeing. This study also provides a valuable reference for other related studies.
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Mukti, Mousumi Hasan, Quazi Saad-Ul-Mosaher, and Khalil Ahammad. "Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric." European Journal of Engineering Research and Science 3, no. 7 (2018): 67. http://dx.doi.org/10.24018/ejers.2018.3.7.831.

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Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.
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Mukti, Mousumi Hasan, Quazi Saad-Ul-Mosaher, and Khalil Ahammad. "Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric." European Journal of Engineering and Technology Research 3, no. 7 (2018): 67–73. http://dx.doi.org/10.24018/ejeng.2018.3.7.831.

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Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.
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Dissertations / Theses on the topic "Handwritten character recognition system (HCR)"

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KUMAR, PRAJJWAL. "HANDWRITTEN CHARACTER RECOGNITION USING DEEP LEARNING." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19136.

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Handwritten character identification is a topic that has been researched for years and is an area of interest for the community of Pattern recognition researchers since It may be put to use in a wide range of fascinating applications. all across the field. This subject is a difficult challenge as a task because each person has their own unique writing style. SVM, ANN, and CNN models are some of the available options for handling this problem's many different ways and approaches. HCR is a need in the modern world since it assists us in a variety of fields of public domain, which makes it all the more vital to study in depth. Off-line digit recognition and online digit recognition are both examples of the hybrid character recognition (HCR) category. In this study, we review the many existing algorithms that have been implemented to get the better knowledge of the course, and we will come to a conclusion on the best strategies that are currently being developed for HCR. HCR for Devanagari is carried out by the performance of a computational device that accepts input from documents, screens, photos, and other responsive devices and believe to provides output by reading those images as an ASCII or UNICODE format. This theory is supported by the fact that computers have become increasingly powerful in recent years. Sanskrit, Nepali, Marathi, and Hindi are some of the languages that are represented in Devanagari. This script is a blend of numerous languages. This implementation is more important because the design of upper-case and lower-case characters in Devanagari are more complicated than in most other languages out there. Comparatively speaking, the set of characters and digits used in Devanagari is more complicated than the set of characters used in the English language. Character recognition has been hampered by the absence of verified datasets including Devanagari, which has made the task more difficult to do in the field.
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Cho, Cheng-Ming, and 卓正民. "Fuzzy Rule-Based Handwritten Chinese Character Recognition System." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/15293821733202088575.

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碩士<br>大同大學<br>電機工程研究所<br>85<br>The goal of this thesis is to develop a handwritten Chinese character recognition system based on the fuzzy logic theory. Because of the fact that fuzzy theory is found to be naturally effective for any human-like cognition systems and can deal with noisy and imprecise information effectively, it can be applied to pattern or handwritten character systems with vagueness and uncertainty. The architecture of the developed system is simpler than traditional methods and is robust against vagueness. At first, we introduce how to describe the characteristics of characters using fuzzy variables and how to extract the useful fuzzy features. Then we build the fuzzy rules and finally the handwritten Chinese character recognition system is constructed completely. Simulations demonstrate that the proposed system is effective, robust and has high recogniton rate.
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Lin, Yi-Ling, and 林倚鈴. "Video Handwritten Chinese Character Recognition System Using Stroke Segmentation." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/47803280429289060109.

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碩士<br>大同大學<br>資訊工程學系(所)<br>98<br>In recent years, traditional text input devices are changed gradually to handwriting. Though handwriting board and touchpad are easy to use, they are inconvenient to carry. This paper proposes a video handwritten Chinese character recognition system using stroke segmentation and on-line model. Firstly, the location of fingertip is extracted and the fingertip trajectory is recorded for recognition. The trajectory is straight line approximated by finding the turning points of strokes. Owing to the loss of depth information, it is unknown the user is going to write or move the fingertip. Therefore ,character writing habits are utilized to develop rules for pen-up and pen-down strokes classification. The main idea is to find impossible strokes which represent pen-up strokes for moving fingertip. The first case of pen-up stroke is from right to left or bottom to up. The second case is that the pen-up stroke would not exist between two parallel strokes. The third case is that the next pen-down stroke would not be of the opposite direction with the current one. The forth case is pen-up stroke between left and right character components. And the last case is pen-up stroke between upper on lower character component. These rules are used to segment the character into pen-down and pen-up strokes. In addition to stroke direction, stroke types, stroke length ratio, and angle between two consecutive strokes are also used to build the character online model as a four tuple continuous sequence of string. For characters written with multiple ways, we could build more than one online model for these characters. Minimum edit distance by dynamic programming is deployed to match the input character on-line string with stored online character models for recognition. In experiments, we build about 1000 Chinese character on-line models. The recognition system is tested with five persons writing each of the 1000 Chinese characters three times. There are totally 548726 images with camera of capturing speed 30 frames per second. The accuracy of fingertip tracking is 98.88% with processing speed 12.6 times per second. The accuracy of pen-up and pen-down stroke segmentation is 91.56% and the accuracy of character recognition is 93.61%. These results demonstrate that the proposed method could be used to input characters by fingertip efficiently.
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ZHENG, DING-SHAN, and 鄭頂山. "A neural network system for handwritten chinese character recognition." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/56144428308627649232.

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Lin, Tsung-Yih, and 林聰義. "Design and Implementation of a Handwritten Chinese Character Recognition System." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/45914576192659976301.

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碩士<br>國立中興大學<br>資訊科學學系<br>83<br>Optical character recognition(OCR) has been a subject of great interest to many people. It is very important in realize the dream of automatic reading of texts from a document. There are no perfect OCR for handwritten Chinese characters. Typically, a OCR system is divided into three stages: preprocessing, feature extraction, and recognition. Globally speaking, a good OCR system is only decided by its recognition rate. However, good result of recognition is dependent on good feature extraction which is dependent on good preprocessing. In this thesis, we design and implement every stage. The feature extraction of our system is based on 4-corner number proposed by Uing-Wu Wang.
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Books on the topic "Handwritten character recognition system (HCR)"

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Chang, Iris J. A handwritten numeral recognition system with multi-level decision scheme (MDS). 1986.

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Book chapters on the topic "Handwritten character recognition system (HCR)"

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Sigappi, AN, and S. Palanivel. "AANN-Based Online Handwritten Tamil Character Recognition." In Recent Advancements in System Modelling Applications. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1035-1_4.

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Wang, Xianjing, and Atul Sajjanhar. "Polar Transformation System for Offline Handwritten Character Recognition." In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22288-7_2.

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Jindal, Udit, Sheifali Gupta, Vishal Jain, and Marcin Paprzycki. "Offline Handwritten Gurumukhi Character Recognition System Using Deep Learning." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0339-9_11.

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Prapitasari, Luh Putu Ayu, and Komang Budiarta. "Direction and Semantic Features for Handwritten Balinese Character Recognition System." In Proceedings of Second International Conference on Electrical Systems, Technology and Information 2015 (ICESTI 2015). Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-988-2_15.

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Wang, Yanwen. "Online Handwritten Chinese Character Recognition Simulation System Based on Web." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2568-1_249.

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Patel, Meghna B., Satyen M. Parikh, and Ashok R. Patel. "Comparative Study of Handwritten Character Recognition System for Indian Languages." In ICT with Intelligent Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4177-0_78.

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Sharma, Ankit K., Dipak M. Adhyaru, and Tanish H. Zaveri. "A Novel Cross Correlation-Based Approach for Handwritten Gujarati Character Recognition." In Proceedings of First International Conference on Smart System, Innovations and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5828-8_48.

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Xia, Taiwu, and Bang Zhou. "Recognition of Handwritten Chinese Character Based on Least Square Support Vector Machine." In Advances in Computer Science, Intelligent System and Environment. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23753-9_36.

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Mitrpanont, J. L., and Surasit Kiwprasopsak. "The Development of the Feature Extraction Algorithms for Thai Handwritten Character Recognition System." In Developments in Applied Artificial Intelligence. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48035-8_52.

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Choudhary, Amit, Savita Ahlawat, and Rahul Rishi. "A Neural Approach to Cursive Handwritten Character Recognition Using Features Extracted from Binarization Technique." In Complex System Modelling and Control Through Intelligent Soft Computations. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12883-2_26.

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Conference papers on the topic "Handwritten character recognition system (HCR)"

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Qader, Islam A., and Tarik A. Rashid. "Kurdish Handwritten Character Recognition System: Review of Methods and Progress." In 2024 10th International Engineering Conference on Advances in Computer and Civil Engineering (IEC). IEEE, 2024. https://doi.org/10.1109/iec61018.2024.11063881.

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Wahid Khandakhani, Shazid, Sachikanta Dash, Sasmita Padhy, and Rabinarayan Panda. "Implementation of Customized Convolutional Neural Networks for Handwritten Marathi Character Recognition." In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2024. https://doi.org/10.1109/scopes64467.2024.10990953.

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Gowda, Deekshith K., and V. Kanchana. "Expression of Concern for: Kannada Handwritten Character Recognition and Classification Through OCR Using Hybrid Machine Learning Techniques." In 2022 IEEE International Conference on Data Science and Information System (ICDSIS). IEEE, 2022. http://dx.doi.org/10.1109/icdsis55133.2022.10703484.

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Lai, Yun. "Online Handwritten Chinese Character Recognition and Real-Time Error Correction Based on Deep Learning and Intelligent System." In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON). IEEE, 2024. http://dx.doi.org/10.1109/nmitcon62075.2024.10699302.

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Rajyagor, Bhargav, and Rajnish Rakholia. "Isolated Gujarati Handwritten Character Recognition (HCR) using Deep Learning (LSTM)." In 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2021. http://dx.doi.org/10.1109/icecct52121.2021.9616652.

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Fan, Fang, and Zhen Yong Lin. "Online handwritten Chinese character recognition system." In Electronic Imaging, edited by Daniel P. Lopresti and Jiangying Zhou. SPIE, 1999. http://dx.doi.org/10.1117/12.373505.

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Rushiraj, Indugu, Souvik Kundu, and Baidyanath Ray. "Handwritten character recognition of Odia script." In 2016 International conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2016. http://dx.doi.org/10.1109/scopes.2016.7955542.

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Singh, Dayashankar, Maitreyee Dutta, and Sarvpal H. Singh. "Neural network based handwritten hindi character recognition system." In the 2nd Bangalore Annual Compute Conference. ACM Press, 2009. http://dx.doi.org/10.1145/1517303.1517320.

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Sanjrani, Anwar Ali, Junaid Baber, Maheen Bakhtyar, Waheed Noor, and Muhammad Khalid. "Handwritten Optical Character Recognition system for Sindhi numerals." In 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube). IEEE, 2016. http://dx.doi.org/10.1109/icecube.2016.7495235.

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Moni, Bindu S., and G. Raju. "Handwritten character recognition system using a simple feature." In the International Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2345396.2345515.

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You might also be interested in the extended bibliographies on the topic 'Handwritten character recognition system (HCR)' for particular source types:
Journal articles
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