General Detection-based Text Line Recognition

• URL: http://arxiv.org/abs/2409.17095v1
• Date: Wed, 25 Sep 2024 17:05:55 GMT
• Title: General Detection-based Text Line Recognition
• Authors: Raphael Baena, Syrine Kalleli, Mathieu Aubry,
• Abstract summary: We introduce a general detection-based approach to text line recognition, be it printed (OCR) or handwritten (HTR) Our approach builds on a completely different paradigm than state-of-the-art HTR methods, which rely on autoregressive decoding. We improve state-of-the-art performances for Chinese script recognition on the CASIA v2 dataset, and for cipher recognition on the Borg and Copiale datasets.
• Score: 15.761142324480165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a general detection-based approach to text line recognition, be it printed (OCR) or handwritten (HTR), with Latin, Chinese, or ciphered characters. Detection-based approaches have until now been largely discarded for HTR because reading characters separately is often challenging, and character-level annotation is difficult and expensive. We overcome these challenges thanks to three main insights: (i) synthetic pre-training with sufficiently diverse data enables learning reasonable character localization for any script; (ii) modern transformer-based detectors can jointly detect a large number of instances, and, if trained with an adequate masking strategy, leverage consistency between the different detections; (iii) once a pre-trained detection model with approximate character localization is available, it is possible to fine-tune it with line-level annotation on real data, even with a different alphabet. Our approach, dubbed DTLR, builds on a completely different paradigm than state-of-the-art HTR methods, which rely on autoregressive decoding, predicting character values one by one, while we treat a complete line in parallel. Remarkably, we demonstrate good performance on a large range of scripts, usually tackled with specialized approaches. In particular, we improve state-of-the-art performances for Chinese script recognition on the CASIA v2 dataset, and for cipher recognition on the Borg and Copiale datasets. Our code and models are available at https://github.com/raphael-baena/DTLR.

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