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Relevant bibliographies by topics / Devanagari online handwritten characters

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Academic literature on the topic 'Devanagari online handwritten characters'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Devanagari online handwritten characters"

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Ghosh, Rajib, and Prabhat Kumar. "SVM and HMM Classifier Combination Based Approach for Online Handwritten Indic Character Recognition." Recent Advances in Computer Science and Communications 13, no. 2 (2020): 200–214. http://dx.doi.org/10.2174/2213275912666181127124711.

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Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten num

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A., Bharath, and Sriganesh Madhvanath. "Allograph modeling for online handwritten characters in devanagari using constrained stroke clustering." ACM Transactions on Asian Language Information Processing 13, no. 3 (2014): 1–21. http://dx.doi.org/10.1145/2629622.

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SANTOSH, K. C., CHOLWICH NATTEE, and BART LAMIROY. "RELATIVE POSITIONING OF STROKE-BASED CLUSTERING: A NEW APPROACH TO ONLINE HANDWRITTEN DEVANAGARI CHARACTER RECOGNITION." International Journal of Image and Graphics 12, no. 02 (2012): 1250016. http://dx.doi.org/10.1142/s0219467812500167.

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In this paper, we propose a new scheme for Devanagari natural handwritten character recognition. It is primarily based on spatial similarity-based stroke clustering. A feature of a stroke consists of a string of pen-tip positions and directions at every pen-tip position along the trajectory. It uses the dynamic time warping algorithm to align handwritten strokes with stored stroke templates and determine their similarity. Experiments are carried out with the help of 25 native writers and a recognition rate of approximately 95% is achieved. Our recognizer is robust to a large range of writing s

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Samanta, Roopkatha, Soulib Ghosh, Agneet Chatterjee, and Ram Sarkar. "A Novel Approach Towards Handwritten Digit Recognition Using Refraction Property of Light Rays." International Journal of Computer Vision and Image Processing 10, no. 3 (2020): 1–17. http://dx.doi.org/10.4018/ijcvip.2020070101.

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Due to the enormous application, handwritten digit recognition (HDR) has become an extremely important domain in optical character recognition (OCR)-related research. The predominant challenges faced in this domain include different photometric inconsistencies together with computational complexity. In this paper, the authors proposed a language invariant shape-based feature descriptor using the refraction property of light rays. It is to be noted that the proposed approach is novel as an adaptation of refraction property is completely new in this domain. The proposed method is assessed using

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Srivastav, Ankita, and Neha Sahu. "Segmentation of Devanagari Handwritten Characters." International Journal of Computer Applications 142, no. 14 (2016): 15–18. http://dx.doi.org/10.5120/ijca2016909994.

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Yadav, Bharati, Ajay Indian, and Gaurav Meena. "HDevChaRNet: A deep learning-based model for recognizing offline handwritten devanagari characters." Journal of Autonomous Intelligence 6, no. 2 (2023): 679. http://dx.doi.org/10.32629/jai.v6i2.679.

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<p>Optical character recognition (OCR) converts text images into machine-readable text. Due to the non-availability of several standard datasets of Devanagari characters, researchers have used many techniques for developing an OCR system with varying recognition rates using their own created datasets. The main objective of our proposed study is to improve the recognition rate by analyzing the effect of using batch normalization (BN) instead of dropout in convolutional neural network (CNN) architecture. So, a CNN-based model HDevChaRNet (Handwritten Devanagari Character Recognition Networ

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MALIK, LATESH, and P. S. DESHPANDE. "RECOGNITION OF HANDWRITTEN DEVANAGARI SCRIPT." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (2010): 809–22. http://dx.doi.org/10.1142/s0218001410008123.

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Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper, we propose a float fill algorithm for segmentation of unconstrained Devanagari text into words. Here, a text image is directly segmented into individual words. Rectangular boundaries are drawn around the words and horizontal lines are detected with template matching. A mask is designed for detecting the horizontal line and is applied to each word from left to right and top to bottom of the document. Header lines are removed for character se

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Padmaja, Kannuru. "Devanagari Handwritten Character Recognition Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 102–5. http://dx.doi.org/10.22214/ijraset.2022.39744.

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Abstract: In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the

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Kapoor, Shuchi, and Vivek Verma. "Fragmentation of Handwritten Touching Characters in Devanagari Script." International Journal of Information Technology, Modeling and Computing 2, no. 1 (2014): 11–21. http://dx.doi.org/10.5121/ijitmc.2014.2102.

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Deore, Shalaka P. "DHCR_SmartNet: A smart Devanagari Handwritten Character Recognition using Level-wised CNN Architecture." Computer Science 23, no. 3 (2022): 303. http://dx.doi.org/10.7494/csci.2022.23.3.4487.

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Handwritten Script Recognition is a vital application of Machine Learning domain. Applications like automatic number plate detection, pin code detection and managing historical documents increasing more attention towards handwritten script recognition. English is the most widely spoken language, hence there has been a lot of research into identifying a script using a machine. Devanagari is popular script used by a huge number of people in the Indian Subcontinent. In this paper, level-wised efficient transfer learning approach presented on VGG16 model of Convolutional Neural Network (CNN) for i

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Dissertations / Theses on the topic "Devanagari online handwritten characters"

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Wang, Yongqiang, and 王永強. "A study on structured covariance modeling approaches to designing compact recognizers of online handwritten Chinese characters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42664305.

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Wang, Yongqiang. "A study on structured covariance modeling approaches to designing compact recognizers of online handwritten Chinese characters." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42664305.

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Sharma, Anand. "Devanagari Online Handwritten Character Recognition." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4633.

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In this thesis, a classifier based on local sub-unit level and global character level representations of a character, using stroke direction and order variations independent features, is developed for recognition of Devanagari online handwritten characters. It is shown that online character corresponding to Devanagari ideal character can be analyzed and uniquely represented in terms of homogeneous sub-structures called the sub-units. These sub-units can be extracted using direction property of online strokes in an ideal character. A method for extraction of sub-units from a handwritten c

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Book chapters on the topic "Devanagari online handwritten characters"

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Chakraborty, Rajatsubhra, Soumyajit Saha, Ankan Bhattacharyya, Shibaprasad Sen, Ram Sarkar, and Kaushik Roy. "Recognition of Online Handwritten Bangla and Devanagari Basic Characters: A Transfer Learning Approach." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1092-9_45.

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Swethalakshmi, H., C. Chandra Sekhar, and V. Srinivasa Chakravarthy. "Spatiostructural Features for Recognition of Online Handwritten Characters in Devanagari and Tamil Scripts." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74695-9_24.

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Singh, Rajdeep, Arvind Kumar Shukla, Rahul Kumar Mishra, and S. S. Bedi. "An Improved Approach for Devanagari Handwritten Characters Recognition System." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2008-9_20.

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Doiphode, Akshata, and Leena Ragha. "Novel Approach for Segmentation of Handwritten Touching Characters from Devanagari Words." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25734-6_106.

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Duddela, Sai Prashanth, Senthil Kumaran, and Priya R. Kamath. "Analysis on Classification of Handwritten Devanagari Characters Using Deep Learning Models." In Applications and Techniques in Information Security. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2264-2_18.

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Ma, Long-Long, and Jian Wu. "A Recognition System for Online Handwritten Tibetan Characters." In Graphics Recognition. New Trends and Challenges. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36824-0_10.

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Li, Yunchao, and Jiangqing Wang. "A New Approach to Recognize Online Handwritten NǚShu Characters." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29387-0_30.

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Singh, Sukhdeep, and Anuj Sharma. "Recognition of Online Handwritten Gurmukhi Characters Through Neural Networks." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5341-7_18.

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Sen, Shibaprasad, Ram Sarkar, Kaushik Roy, and Naoto Hori. "Recognize Online Handwritten Bangla Characters Using Hausdorff Distance-Based Feature." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3153-3_54.

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Yamaguchi, Tatsuya, Noriaki Muranaka, and Masataka Tokumaru. "Penmanship Learning Support System: Feature Extraction for Online Handwritten Characters." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15399-0_71.

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Conference papers on the topic "Devanagari online handwritten characters"

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Joshi, N., G. Sita, A. G. Ramakrishnan, V. Deepu, and S. Madhvanath. "Machine recognition of online handwritten Devanagari characters." In Eighth International Conference on Document Analysis and Recognition (ICDAR'05). IEEE, 2005. http://dx.doi.org/10.1109/icdar.2005.156.

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Kubatur, Shruthi, Maher Sid-Ahmed, and Majid Ahmadi. "A neural network approach to online Devanagari handwritten character recognition." In 2012 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2012. http://dx.doi.org/10.1109/hpcsim.2012.6266913.

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Chakraborty, Rajatsubhra, Debadrita Mukherjee, Ankan Bhattacharyya, et al. "Online Handwritten Bangla and Devanagari Character Recognition by using CNN: A Deep Learning Concept." In 2020 IEEE International Conference for Convergence in Engineering (ICCE). IEEE, 2020. http://dx.doi.org/10.1109/icce50343.2020.9290566.

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Shitole, Sneha, and Savitri Jadhav. "Recognition of handwritten devanagari characters using linear discriminant analysis." In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 2018. http://dx.doi.org/10.1109/icisc.2018.8398991.

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Lajish, V. L., and Sunil Kumar Kopparapu. "Online handwritten devanagari stroke recognition using extended directional features." In 2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS). IEEE, 2014. http://dx.doi.org/10.1109/icspcs.2014.7021063.

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Keshri, Pooja, Prabhat Kumar, and Rajib Ghosh. "RNN Based Online Handwritten Word Recognition in Devanagari Script." In 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2018. http://dx.doi.org/10.1109/icfhr-2018.2018.00096.

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Thakral, Binny, and Manoj Kumar. "Devanagari handwritten text segmentation for overlapping and conjunct characters- A proficient technique." In 2014 3rd International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions). IEEE, 2014. http://dx.doi.org/10.1109/icrito.2014.7014746.

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Nguyen, Duy Khuong, and The Duy Bui. "Recognizing Vietnamese Online Handwritten Separated Characters." In 2008 International Conference on Advanced Language Processing and Web Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/alpit.2008.58.

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Kinjarapu, Ananda Kumar, Kalyan Chakravarti Yelavarti, and Kamakshi Prasad Valurouthu. "Online recognition of handwritten Telugu script characters." In 2016 International conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2016. http://dx.doi.org/10.1109/scopes.2016.7955866.

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Golubitsky, Oleg, and Stephen M. Watt. "Online computation of similarity between handwritten characters." In IS&T/SPIE Electronic Imaging, edited by Kathrin Berkner and Laurence Likforman-Sulem. SPIE, 2009. http://dx.doi.org/10.1117/12.806163.

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