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arXiv · 2201.09390

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

Dmitrijs Kass·Ekta Vats
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Abstract

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.

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Dmitrijs Kass, Ekta Vats. 2022-01-23. AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks. https://arxiv.org/abs/2201.09390

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