Summary of Hatformer: Historic Handwritten Arabic Text Recognition with Transformers, by Adrian Chan et al.
HATFormer: Historic Handwritten Arabic Text Recognition with Transformers
by Adrian Chan, Anupam Mijar, Mehreen Saeed, Chau-Wai Wong, Akram Khater
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for Arabic handwritten text recognition (HTR) is proposed by leveraging transformer-based architecture. Building on state-of-the-art English HTR models, the HATFormer model captures spatial contextual information through attention mechanisms to address challenges posed by Arabic script. The customization includes an image processor, text tokenizer, and training pipeline tailored to limited historical Arabic handwriting data. Evaluation metrics show a significant improvement over baselines, with character error rates of 8.6% on the largest public dataset and 4.2% on the private non-historical dataset. This work demonstrates the feasibility of adapting English HTR methods for low-resource languages with complex challenges, contributing to advancements in document digitization, information retrieval, and cultural preservation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Arabic handwriting recognition is tricky because of different writing styles and the unique features of Arabic script. There are fewer datasets available for Arabic than for English, making it hard to train good models. A new approach called HATFormer uses transformers to recognize handwritten text. It captures information about the spatial context of characters, which helps with recognizing cursive letters and diacritics. The model is customized for historical handwritten Arabic texts by preprocessing images, tokenizing text, and training on limited data. The results show that HATFormer can accurately recognize text with a character error rate of 8.6% on one dataset and 4.2% on another. |
Keywords
» Artificial intelligence » Attention » Tokenizer » Transformer
