mlsanityData to 7 Oct 2026

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

arXiv 2201.09390 ↗First posted 23 Jan 2022cs.CV cs.LGcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 14 of its 22 references that are in the map. First posted on arXiv 23 Jan 2022.

Builds on

14 of 22 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  9. 2019What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysiscited by 191
  10. 2019Evaluating Sequence-to-Sequence Models for Handwritten Text Recognitioncited by 58
  11. 2021TextStyleBrush: Transfer of Text Aesthetics from a Single Examplecited by 48
  12. 2016Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attentioncited by 45
2 more
  1. 2018An Efficient End-to-End Neural Model for Handwritten Text Recognitioncited by 25
  2. 2019Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecturecited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 3; 2025: 1; 2026: 4.

12 papers.

  1. 2023OCRBench: On the Hidden Mystery of OCR in Large Multimodal Modelscited by 466
  2. 2024MathWriting: A Dataset For Handwritten Mathematical Expression Recognitioncited by 20
  3. 2025Handwritten Text Recognition: A Surveycited by 12
  4. 2024On the Generalization of Handwritten Text Recognition Modelscited by 10
  5. 2024Spatial Context-based Self-Supervised Learning for Handwritten Text Recognitioncited by 1
  6. 2026CONSTANT: Towards High-Quality One-Shot Handwriting Generation with Patch Contrastive Enhancement and Style-Aware Quantizationcited by 1
  7. 2022Enhancing Indic Handwritten Text Recognition Using Global Semantic Informationcited by 0
  8. 2023Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognitioncited by 0
  9. 2023CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Datasetcited by 0
  10. 2026Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogiescited by 0
  11. 2026SpiS-GAN: Spiral-Modulated Handwriting Synthesis with Star Operationcited by 0
  12. 2026Handwritten Text Recognition Lives in the High-Pixel Variance Subspacecited by 0