Please use this identifier to cite or link to this item: http://10.9.150.37:8080/dspace//handle/atmiyauni/2023
Title: A Comprehensive Survey on Handwritten Gujarati Character and Its Modifier Recognition Methods
Authors: Doshi, Priyank D.
Vanjara, Pratik A.
Keywords: Support vector machine
Bayes probability model
Deterministic finite automaton
Hidden Markov model
Convolutional neural network
Issue Date: 2021
Publisher: Springer Nature Singapore: Information and Communication Technology for Competitive Strategies (ICTCS 2020) ICT: Applications and Social Interfaces
Citation: Doshi, P. D., & Vanjara, P. A. (2021). A Comprehensive Survey on Handwritten Gujarati Character and Its Modifier Recognition Methods. Information and Communication Technology for Competitive Strategies (ICTCS 2020) ICT: Applications and Social Interfaces, 841-850.
Abstract: In India, handwritten character recognition is becoming necessity regional wise due to new education policy 2020. Various technologies are applied to solve the problem in this area like statistical or probability model, support vector machine, Bayes probability model, deterministic finite automaton (DFA), hidden Markov model, and many more which are used. Due to the advancement in machine learning, convolutional neural network is a good solution of HCR which gives more promising results but any new algorithm in machine learning that depends on training data, mathematical function, loss function, and method of evaluation of model. Focusing on past research of handwritten Gujarati character recognition is found that sufficient research is required for modifier level called “Barakshari”. Results obtained in past are limited to character level only. In this paper, our effort is to analyze and summarize previous contributions in the handwritten character recognition for several Indian languages
URI: http://10.9.150.37:8080/dspace//handle/atmiyauni/2023
Appears in Collections:01. Journal Articles

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