A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet
This addresses the problem of accurately recognizing handwritten text in paragraphs for applications like document digitization, though it is incremental as it builds on existing methods.
The paper tackles handwritten paragraph text recognition by combining a Vertical Attention Network for line segmentation with a Word Beam Search decoder, achieving state-of-the-art results with character error rates of 3.24% on IAM, 1.13% on RIMES, and 2.43% on READ-16, and word error rates of 8.29% on IAM, 2.94% on RIMES, and 7.35% on READ-16.
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.