# A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet

arXiv: https://arxiv.org/abs/2205.11018 · HTML: https://mlsanity.com/paper/2205.11018/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 6 of 6)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold](https://mlsanity.com/paper/2006.07491/) (2020)
- [End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network](https://mlsanity.com/paper/2012.03868/) (2020)
- [Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition](https://mlsanity.com/paper/1604.08352/) (2016)
- [Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention](https://mlsanity.com/paper/1604.03286/) (2016)

## Built on by (most cited first, 4 of 4)

- [Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation](https://mlsanity.com/paper/2310.16809/) (2023)
- [GatedLexiconNet: A Comprehensive End-to-End Handwritten Paragraph Text Recognition System](https://mlsanity.com/paper/2404.14062/) (2024)
- [RVAFM: Re-parameterizing Vertical Attention Fusion Module for Handwritten Paragraph Text Recognition](https://mlsanity.com/paper/2503.03104/) (2025)
- [Attend to what I say: Highlighting relevant content on slides](https://mlsanity.com/paper/2601.10244/) (2026)

## Abstract (arXiv)

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.