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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 (June 3, 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 numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

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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 (October 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 (April 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 style and handles variation in the number of strokes, their order, shapes and sizes and similarities among classes.

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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 (July 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 five datasets of five different languages. Among the five datasets, four are offline (written Devanagari, Bangla, Arabic, and Telugu) and one is online (written in Assamese) handwritten digit datasets. The approach provides admirable outcomes for online digits whereas; it yields satisfactory results for offline handwritten digits. The method gives good result for both online and offline handwritten digits, which proves its robustness. It is also computationally less expensive compared to other state-of-the-art methods including deep learning-based models.

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Srivastav, Ankita, and Neha Sahu. "Segmentation of Devanagari Handwritten Characters." International Journal of Computer Applications 142, no. 14 (May 18, 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 (August 15, 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 Network) is proposed in this study for same to recognize offline handwritten Devanagari characters using a dataset named Devanagari handwritten character dataset (DHCD). DHCD comprises a total of 46 classes of characters, out of which 36 are consonants, and 10 are numerals. The proposed models based on convolutional neural network (CNN) with BN for recognizing the Devanagari characters showed an improved accuracy of 98.75%, 99.70%, and 99.17% for 36, 10, and 46 classes, respectively.</p>

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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 (August 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 separation. A new segment code features are extracted for each character. In this paper, we present the results of multiple classifier combination for offline handwritten Devanagari characters. The use of regular expressions in handwritten characters is a novel concept and they are defined in a manner so that they can become more robust to noise. We have achieved an accuracy of 94% for word level segmentation, 95% for coarse classification and 85% for fine classification of character recognition. On experimentation with a dataset of 5000 samples of characters, the overall recognition rate observed is 95% as we considered top five choice results. The proposed combined classifier can be applied to handwritten character recognition of any other language like English, Chinese, Arabic, etc. and can recognize the characters with same accuracy.18 For printed characters we have achieved accuracy of 100%, only by applying the regular expression classifier.17

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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 (January 31, 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 model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

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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 (February 28, 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 (October 2, 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 identification of Devanagari isolated handwritten characters. In this work a new dataset of Devanagari characters is presented and made accessible publicly. Newly created dataset comprises 5800 samples for 12 vowels, 36 consonants and 10 digits. Initially simple CNN is implemented and trained on this new small dataset. In next stage transfer learning approach is implemented on VGG16 model and in last stage fine-tuned efficient VGG16 model is implemented. The training and testing accuracy of fine-tuned model are obtained as 98.16% and 96.47% respectively.

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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 character is developed, such that the extracted sub-units are similar to the sub-units of the corresponding ideal character. Features are developed that are independent of variations in order and direction of strokes in characters. The features are called histograms of points, orientations, and dynamics of orientations (HPOD) features. The method for extraction of these features spatially maps co-ordinates of points and orientations and dynamics of orientations of strokes at these points. Histograms of these mapped features are computed in di erent regions into which the spatial map is divided. HPOD features extracted from the sub-units represent the character locally; and those extracted from the character as a whole represent it globally. A classifier is developed that models handwritten characters in terms of the joint distribution of the local and global HPOD features of the characters and the number of sub-units in the characters. The classifier uses latent variables to model the structure of the the sub-units. The parameters of the model are estimated using the maximum likelihood method. The use of HPOD features and the assumption of independent generation of the sub-units given the number of sub-units, make the classifier independent of variations in the direction and order of strokes in characters. This sub-unit based classifier is called SUB classifier. Datasets for training and testing the classifiers consist of handwritten samples of Devanagari vowels, consonants, half consonants, nasalization sign, vowel omission sign, vowel signs, consonant with vowel sign, conjuncts, consonant clusters, and three more short strokes with di erent shapes. In all, there are 96 di erent characters or symbols that have been considered for recognition. The average number of samples per character class in the training and the test sets are, respectively, 133 and 29. The smallest and the largest dimensions of the extracted feature vectors are, respectively, 258 and 786. Since the size of the training set per class is not large compared to the dimension of the extracted feature vectors, the training set is small from the perspective of training any classifier. classifiers that can be trained on a small data set are considered for performance comparison with the developed classifier. Second order statistics (SOS), sub-space (SS), Fisher discriminant (FD), feedforward neural network (FNN), and support vector machines (SVM) are the other classifiers considered that are trained with the other features like spatio-temporal (ST), discrete Fourier transform (DFT), discrete cosine transform (DCT), discrete wavelet transform (DWT), spatial (SP), and histograms of oriented gradients (HOG) features extracted from the samples of the training set. These classifiers are tested with these features extracted from the samples of the test set. SVM classifier trained with DFT features has the highest accuracy of 90.2% among the accuracies of the other classifiers trained with the other features extracted from the test set. The accuracy of SUB classifier trained with HPOD features is 92.9% on the test set which is the highest among the accuracies of all the classifiers. The accuracies of the classifiers SOS, SS, FD, FNN, and SVM increase when trained with HPOD features. The accuracy of SVM classifier trained with HPOD features is 92.9%, which is the highest among the accuracies of the other classifiers trained with HPOD features. SUB classifier using HPOD features has the highest accuracy among the considered classifiers trained with the considered features on the same training set and tested on the same test set. The better character discriminative capability of the designed HPOD features is re ected by the increase in the accuracies of the other classifiers when trained with these features

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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, 530–41. Singapore: 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, 230–39. Berlin, Heidelberg: 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, 217–26. Singapore: 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, 621–23. Berlin, Heidelberg: 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, 227–40. Singapore: 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, 99–107. Berlin, Heidelberg: 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, 193–99. Berlin, Heidelberg: 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, 223–34. Singapore: 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, 541–49. Singapore: 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, 496–98. Berlin, Heidelberg: 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, Himadri Mukherjee, Monoj Kumar Sur, Shibaprasad Sen, and Kaushik Roy. "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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