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Relevant bibliographies by topics / Handwritten Devnagri character recognition

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Academic literature on the topic 'Handwritten Devnagri character recognition'

Author: Grafiati

Published: 3 June 2025

Last updated: 12 July 2025

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Journal articles on the topic "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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<strong>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.</strong> <strong>https://www.ijiert.org/paper-details?paper_id=141157</strong>

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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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Dissertations / Theses on the topic "Handwritten Devnagri character recognition"

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Chai, Sin-Kuo. "Multiclassifier neural networks for handwritten character recognition." Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1174331633.

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Clarke, Eddie. "A novel approach to handwritten character recognition." Thesis, University of Nottingham, 1995. http://eprints.nottingham.ac.uk/14035/.

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A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules.

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Xu, Zhengyan, and Yibing Zhou. "Specific Handwritten Chinese Character Recognition Based on Artificial Intelligence." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-14599.

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As internet techniques are developing more and more quickly, internet becomes the main way to communicate with the outside world. In this case, written information on paper needs to be converted to digital information urgently, increasing the need for handwritten character recognition. The aim of this work is to discuss methods that can be used to recognize handwritten Chinese characters. We study geometric features and clustering of handwritten Chinese characters from three aspects, which are handwritten character preprocessing, feature extraction and clustering. To test the correctness of our method, an application was built that could learn to recognize five medium-hard handwritten Chinese characters by using a neural network.

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Sawhney, Sumeet S. "Distance measurements and their combination in handwritten character recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ59339.pdf.

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Ansari, Nasser. "Handwritten character recognition by using neural network based methods." Ohio : Ohio University, 1992. http://www.ohiolink.edu/etd/view.cgi?ohiou1172080742.

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陳國評 and Kwok-ping Chan. "Fuzzy set theoretic approach to handwritten Chinese character recognition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B30425876.

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Sahai, Anant. "Handwritten character recognition using the minimum description length principle." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11015.

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Shi, Daming. "An active radical approach to handwritten Chinese character recognition." Thesis, University of Southampton, 2002. https://eprints.soton.ac.uk/257379/.

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Wang, Jianguo. "Off-line computer recognition of unconstrained handwritten characters." Thesis, The University of Sydney, 2001. https://hdl.handle.net/2123/27805.

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This thesis presents several techniques for improving the performance of off—line Optical Character Recognition (OCR) systems: broken character mending and recognition, feature extraction methods in OCR and hybrid methods for handwritten numeral recognition. As an application, form document image compression and indexing is also introduced. Broken characters mending techniques are investigated first. A macrostrtrcture analysis (MSA) mending method is proposed based on skeleton and boundary information and macrostructure analysis that investigates the stroke tendency and other properties of handwritten characters. A new skeleton end extension algorithm is also introduced. The MSA mending method is combined with a skeleton-based recognition algorithm to verify its efficiency. Experiment results indicate that significant improvement has been achieved. The feature extraction methods in OCR are analyzed by comparing their effectiveness in different situations. Several factors and their relation with the effectiveness of each feather extraction method are investigated. A dynamic feature extraction method is developed to improve the performance of hybrid OCR systems. Hybrid methods for handwritten numeral recognition are then described, which combine two compensatory recognisers by analyzing their performance for several aspects. The different performances of the two algorithms for broken, connected or slanted numerals. and the rneasurement—level decision provided by the neural network algorithm are detected and combined to develop matching rules for each recognition method. Five combination methods are developed to meet different requirements. Experiments with a large number of testing data show satisfactory results for the approach. Finally, a generic method for compressing and indexing multi—copy form documents is developed using template extraction and matching (TEM) strategies and OCR. De—skewing, location and distortion adjusting of form images are employed to realise the TEM method for practical applications. A statistical template extraction algorithm is developed using greyscale images created by overlapping a number of binary form images. The TEM method exploits the cmnponent—Ievel redundancy found in multi—copy form documents and reaches a high compression rate while keeping the original resolution and readability.

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Manley-Cooke, Peter. "Handwritten character recognition using a multi-classifier neuro-fuzzy framework." Thesis, University of East Anglia, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433914.

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Books on the topic "Handwritten Devnagri character recognition"

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Li, Xiaolin. On-line handwritten Kanji character recognition. University of Birmingham, 1994.

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Hastie, Trevor. Handwritten digit recognition via deformable prototypes. University of Toronto, Dept. of Statistics, 1992.

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Pirlo, Giuseppe, Donato Impedovo, and Michael C. Fairhurst. Advances in Digital Handwritten Signature Processing: A Human Artefact for E-Society. World Scientific Publishing Co Pte Ltd, 2014.

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4

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 Devnagri character recognition"

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Sharma, N., U. Pal, F. Kimura, and S. Pal. "Recognition of Off-Line Handwritten Devnagari Characters Using Quadratic Classifier." In Computer Vision, Graphics and Image Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_72.

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Lehal, G. S., and Nivedan Bhatt. "A Recognition System for Devnagri and English Handwritten Numerals." In Advances in Multimodal Interfaces — ICMI 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-40063-x_58.

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Álvarez, D., R. Fernández, and L. Sánchez. "Stroke Based Handwritten Character Recognition." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28942-2_31.

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Tiwari, Usha, Monika Jain, and Shabana Mehfuz. "Handwritten Character Recognition—An Analysis." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0665-5_18.

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Singh, Harshita, Sudhir Singh, and A. K. Mohapatra. "Handwritten character recognition using CNN." In Data Science & Exploration in Artificial Intelligence. CRC Press, 2025. https://doi.org/10.1201/9781003589273-33.

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Yashoda, S. K. Niranjan, and V. N. Manjunath Aradhya. "Transform-Based Trilingual Handwritten Character Recognition." In Frontiers in Intelligent Computing: Theory and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9920-6_30.

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Fox, Richard, and Steven Brownfield. "Applying Context to Handwritten Character Recognition." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19810-7_5.

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Hogervorst, A. C. R., M. K. van Dijk, P. C. M. Verbakel, and C. Krijgsman. "Handwritten character recognition using neural networks." In Neural Networks: Artificial Intelligence and Industrial Applications. Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3087-1_62.

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Inunganbi, Sanasam, and Robin Singh Katariya. "Transfer Learning for Handwritten Character Recognition." In Intelligent Sustainable Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6369-7_63.

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Singh, Jaisal, Srinivasan Natesan, Marcin Paprzycki, and Maria Ganzha. "Experimenting with Assamese Handwritten Character Recognition." In Big-Data-Analytics in Astronomy, Science, and Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96600-3_16.

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Conference papers on the topic "Handwritten Devnagri character recognition"

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Rodrigues, Anisha P., Akshay Prabhu K, Shailesh U. Acharya, et al. "Local Language Handwritten Character Recognition." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10987295.

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Yimer, Hailemicael Lulseged, Hailegabriel Dereje Degefa, Marco Cristani, and Federico Cunico. "Learning Based Ge'ez Character Handwritten Recognition." In 2024 IEEE International Multi-Conference on Smart Systems & Green Process (IMC-SSGP). IEEE, 2024. https://doi.org/10.1109/imc-ssgp63352.2024.10919767.

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Devi Sri, Gorla Naga, Shaik Khaleel Ahmed, Mandem Srujanasree, and Shaik Shafiya. "A CNN-Based Handwritten English Character Recognition." In 2024 International Conference on Cybernation and Computation (CYBERCOM). IEEE, 2024. https://doi.org/10.1109/cybercom63683.2024.10803137.

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Tapu, Tasmi Khair, Farhan Faiaz, Anika Nawer, and Sadia Rahman Payel. "Bangla Handwritten Character Recognition using Vision Transformer." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11014037.

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

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Pal, U., N. Sharma, T. Wakabayashi, and F. Kimura. "Off-Line Handwritten Character Recognition of Devnagari Script." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007). IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4378759.

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Arora, Sandhya, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, and Mahantapas Kundu. "Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition." In 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems (ICIIS). IEEE, 2008. http://dx.doi.org/10.1109/iciinfs.2008.4798415.

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Shah, Kunal Ravindra, and Dipak Dattatray Badgujar. "Devnagari handwritten character recognition (DHCR) for ancient documents: A review." In 2013 IEEE Conference on Information & Communication Technologies (ICT). IEEE, 2013. http://dx.doi.org/10.1109/cict.2013.6558176.

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Chandure, Savitri Laxmanrao, and Vandana Inamdar. "Performance analysis of handwritten Devnagari and MODI Character Recognition system." In 2016 International Conference on Computing, Analytics and Security Trends (CAST). IEEE, 2016. http://dx.doi.org/10.1109/cast.2016.7915022.

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Sahu, N., and N. K. Raman. "An efficient handwritten Devnagari character recognition system using neural network." In 2013 International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s). IEEE, 2013. http://dx.doi.org/10.1109/imac4s.2013.6526403.

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Reports on the topic "Handwritten Devnagri character recognition"

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Grother, Patrick J. Karhunen Loeve feature extraction for neural handwritten character recognition. National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4824.

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Fuller, J. J., A. Farsaie, and T. Dumoulin. Handwritten Character Recognition Using Feature Extraction and Neural Networks. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada238294.

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Griffiths, Rachael. Transkribus in Practice: Improving CER. Verlag der Österreichischen Akademie der Wissenschaften, 2022. http://dx.doi.org/10.1553/tibschol_erc_cog_101001002_griffiths_cer.

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This paper documents ongoing efforts to enhance the accuracy of Handwritten Text Recognition (HTR) models using Transkribus, focusing on the transcription of Tibetan cursive (dbu med) manuscripts from the 11th to 13th centuries within the framework of the ERC-funded project, The Dawn of Tibetan Buddhist Scholasticism (11th-13th C.) (TibSchol). It presents the steps taken to improve the Character Error Rate (CER) of the HTR models, the results achieved so far, and considerations for those working on similar projects.

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