A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet

• URL: http://arxiv.org/abs/2205.11018v3
• Date: Wed, 5 Jul 2023 06:08:29 GMT
• Title: A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet
• Abstract summary: We have presented an efficient procedure using two state-of-the-art approaches from the literature of handwritten text recognition. The attention module is responsible for internal line segmentation that consequently processes a page in a line-by-line manner. At the decoding step, we have added a connectionist temporal classification-based word beam search decoder as a post-processing step.
• Score: 3.9097549127191473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we have presented an efficient procedure using two state-of-the-art approaches from the literature of handwritten text recognition as Vertical Attention Network and Word Beam Search. The attention module is responsible for internal line segmentation that consequently processes a page in a line-by-line manner. At the decoding step, we have added a connectionist temporal classification-based word beam search decoder as a post-processing step. In this study, an end-to-end paragraph recognition system is presented with a lexicon decoder as a post-processing step. Our procedure reports state-of-the-art results on standard datasets. The reported character error rate is 3.24% on the IAM dataset with 27.19% improvement, 1.13% on RIMES with 40.83% improvement and 2.43% on the READ-16 dataset with 32.31% improvement from existing literature and the word error rate is 8.29% on IAM dataset with 43.02% improvement, 2.94% on RIMES dataset with 56.25% improvement and 7.35% on READ-2016 dataset with 47.27% improvement from the existing results. The character error rate and word error rate reported in this work surpass the results reported in the literature.

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