A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet

TL;DR

This paper introduces LexiconNet, an end-to-end handwritten paragraph recognition system combining vertical attention and word beam search, achieving state-of-the-art accuracy on multiple datasets.

Contribution

It presents a novel integration of attention-based line segmentation with a T-C T-based lexicon decoder for improved handwritten text recognition.

Findings

Character error rate of 3.24% on IAM dataset

Word error rate of 8.29% on IAM dataset

Significant improvements over previous methods on multiple datasets

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.