End-To-End Deep-Learning-Based Tamil Handwritten Document Recognition and Classification Model
Overview: Handwriting recognition (HR) involves converting handwritten text into machine-readable text. Tamil handwritten document recognition remains a challenging process in various real-world applications owing to the differences in the sizes, styles and orientation angles of Tamil alphabets. Pri...
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Format: | Article |
Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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Online Access: | View Fulltext in Publisher View in Scopus |
LEADER | 02972nam a2200313Ia 4500 | ||
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001 | 10.1109-ACCESS.2023.3270895 | ||
008 | 230529s2023 CNT 000 0 und d | ||
020 | |a 21693536 (ISSN) | ||
245 | 1 | 0 | |a End-To-End Deep-Learning-Based Tamil Handwritten Document Recognition and Classification Model |
260 | 0 | |b Institute of Electrical and Electronics Engineers Inc. |c 2023 | |
300 | |a 1 | ||
856 | |z View Fulltext in Publisher |u https://doi.org/10.1109/ACCESS.2023.3270895 | ||
856 | |z View in Scopus |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159698843&doi=10.1109%2fACCESS.2023.3270895&partnerID=40&md5=49c9d0c8896a9914aec941efedf6cd6d | ||
520 | 3 | |a Overview: Handwriting recognition (HR) involves converting handwritten text into machine-readable text. Tamil handwritten document recognition remains a challenging process in various real-world applications owing to the differences in the sizes, styles and orientation angles of Tamil alphabets. Prior studies concentrated only on character-level segmentation, and each character was subsequently classified. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized for Tamil handwritten character recognition (HCR). Objective: This paper attempts to present an end-to-end DL-based Tamil handwritten document recognition (ETEDL-THDR) model. Methods: Segmentation is used, first at the word level and then at the line level. ETEDL-THDR text recognition can be accomplished using two modules: line segmentation and line recognition. Initially, the ETEDL-THDR model targets improving input image quality using the median filtering (MF) technique. To create meaningful regions, more line and character segmentation activities are performed. A deep convolutional neural network (DCNN) based MobileNet approach is also applied to derive feature vectors. Finally, the water strider optimization (WSO) algorithm with a bidirectional gated recurrent unit (BiGRU) model is used to identify the Tamil characters. Results: Extensive experimental analyses of the ETEDL-THDR model have been carried out, and the results show that the ETEDL-THDR model performs better than more recent methodologies, with a maximum accuracy of 98.48%, the precision of 98.38%, sensitivity of 97.98%, specificity of 98.27% and F-measure of 98.35%. Conclusion: The comparison results show that the proposed model can recognize Tamil handwritten documents in real-time. Author | |
650 | 0 | 4 | |a Character recognition |
650 | 0 | 4 | |a Convolutional neural networks |
650 | 0 | 4 | |a deep learning |
650 | 0 | 4 | |a Feature extraction |
650 | 0 | 4 | |a Handwriting recognition |
650 | 0 | 4 | |a Handwritten character recognition |
650 | 0 | 4 | |a Hidden Markov models |
650 | 0 | 4 | |a Image recognition |
650 | 0 | 4 | |a Image segmentation |
650 | 0 | 4 | |a machine learning |
650 | 0 | 4 | |a segmentation |
650 | 0 | 4 | |a Tamil language |
700 | 1 | 0 | |a Lakshmana Pandian, S. |e author |
700 | 1 | 0 | |a Vinotheni, C. |e author |
773 | |t IEEE Access |