# AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks

arXiv: https://arxiv.org/abs/2201.09390 · HTML: https://mlsanity.com/paper/2201.09390/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 23 Jan 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [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)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018)
- [Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition](https://mlsanity.com/paper/1406.2227/) (2014)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition](https://mlsanity.com/paper/1903.07377/) (2019)
- [TextStyleBrush: Transfer of Text Aesthetics from a Single Example](https://mlsanity.com/paper/2106.08385/) (2021)
- [Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention](https://mlsanity.com/paper/1604.03286/) (2016)
- [An Efficient End-to-End Neural Model for Handwritten Text Recognition](https://mlsanity.com/paper/1807.07965/) (2018)
- [Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture](https://mlsanity.com/paper/1912.10308/) (2019)

## Built on by (most cited first, 12 of 12)

- [OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://mlsanity.com/paper/2305.07895/) (2023)
- [MathWriting: A Dataset For Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2404.10690/) (2024)
- [Handwritten Text Recognition: A Survey](https://mlsanity.com/paper/2502.08417/) (2025)
- [On the Generalization of Handwritten Text Recognition Models](https://mlsanity.com/paper/2411.17332/) (2024)
- [Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition](https://mlsanity.com/paper/2404.11585/) (2024)
- [CONSTANT: Towards High-Quality One-Shot Handwriting Generation with Patch Contrastive Enhancement and Style-Aware Quantization](https://mlsanity.com/paper/2603.07543/) (2026)
- [Enhancing Indic Handwritten Text Recognition Using Global Semantic Information](https://mlsanity.com/paper/2212.07776/) (2022)
- [Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition](https://mlsanity.com/paper/2303.05929/) (2023)
- [CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Dataset](https://mlsanity.com/paper/2307.00664/) (2023)
- [Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies](https://mlsanity.com/paper/2604.09713/) (2026)
- [SpiS-GAN: Spiral-Modulated Handwriting Synthesis with Star Operation](https://mlsanity.com/paper/2607.06949/) (2026)
- [Handwritten Text Recognition Lives in the High-Pixel Variance Subspace](https://mlsanity.com/paper/2609.35473/) (2026)

## Abstract (arXiv)

This work proposes an attention-based sequence-to-sequence model for handwritten word recognition and explores transfer learning for data-efficient training of HTR systems. To overcome training data scarcity, this work leverages models pre-trained on scene text images as a starting point towards tailoring the handwriting recognition models. ResNet feature extraction and bidirectional LSTM-based sequence modeling stages together form an encoder. The prediction stage consists of a decoder and a content-based attention mechanism. The effectiveness of the proposed end-to-end HTR system has been empirically evaluated on a novel multi-writer dataset Imgur5K and the IAM dataset. The experimental results evaluate the performance of the HTR framework, further supported by an in-depth analysis of the error cases. Source code and pre-trained models are available at https://github.com/dmitrijsk/AttentionHTR.