2022
DOI: 10.1007/978-981-19-6737-5_22
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OCR for Devanagari Script Using a Deep Hybrid CNN-RNN Network

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“…To develop an optimal vocabulary-based training dataset for multilingual, AI-powered, real-time OCR systems, fostering technical excellence and pushing the boundaries of the research field. Several modern text recognizers are presented in Figure 3, all of which not only have high accuracy but also represent fast inference speeds: (a) CNN-RNN-based models [37]; (b) encoder-decoder models [38] involving multihead selfattention (MHSA) [39] and multihead attention (MHA) mechanisms [40]; (c) visionlanguage models [41]; and (d) SVTR [42], which recognizes scene text by using a single visual model built with cross-lingual capability [7] in mind. 2.…”
Section: Objectivesmentioning
confidence: 99%
“…To develop an optimal vocabulary-based training dataset for multilingual, AI-powered, real-time OCR systems, fostering technical excellence and pushing the boundaries of the research field. Several modern text recognizers are presented in Figure 3, all of which not only have high accuracy but also represent fast inference speeds: (a) CNN-RNN-based models [37]; (b) encoder-decoder models [38] involving multihead selfattention (MHSA) [39] and multihead attention (MHA) mechanisms [40]; (c) visionlanguage models [41]; and (d) SVTR [42], which recognizes scene text by using a single visual model built with cross-lingual capability [7] in mind. 2.…”
Section: Objectivesmentioning
confidence: 99%