MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition
This addresses the challenge of expensive data collection for handwritten text recognition, though it is incremental as it builds on existing transformer architectures.
The paper tackles the problem of data scarcity in full-page multi-script handwriting recognition by proposing a lite transformer model that can be trained on limited datasets without external data, achieving effectiveness across French, English, Spanish, and Arabic scripts.
The Transformer has quickly become the dominant architecture for various pattern recognition tasks due to its capacity for long-range representation. However, transformers are data-hungry models and need large datasets for training. In Handwritten Text Recognition (HTR), collecting a massive amount of labeled data is a complicated and expensive task. In this paper, we propose a lite transformer architecture for full-page multi-script handwriting recognition. The proposed model comes with three advantages: First, to solve the common problem of data scarcity, we propose a lite transformer model that can be trained on a reasonable amount of data, which is the case of most HTR public datasets, without the need for external data. Second, it can learn the reading order at page-level thanks to a curriculum learning strategy, allowing it to avoid line segmentation errors, exploit a larger context and reduce the need for costly segmentation annotations. Third, it can be easily adapted to other scripts by applying a simple transfer-learning process using only page-level labeled images. Extensive experiments on different datasets with different scripts (French, English, Spanish, and Arabic) show the effectiveness of the proposed model.