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arXiv · 2310.00937

Data Efficient Training of a U-Net Based Architecture for Structured Documents Localization

Anastasiia Kabeshova·Guillaume Betmont·Julien Lerouge·Evgeny Stepankevich·Alexis Bergès
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Abstract

Structured documents analysis and recognition are essential for modern online on-boarding processes, and document localization is a crucial step to achieve reliable key information extraction. While deep-learning has become the standard technique used to solve document analysis problems, real-world applications in industry still face the limited availability of labelled data and of computational resources when training or fine-tuning deep-learning models. To tackle these challenges, we propose SDL-Net: a novel U-Net like encoder-decoder architecture for the localization of structured documents. Our approach allows pre-training the encoder of SDL-Net on a generic dataset containing samples of various document classes, and enables fast and data-efficient fine-tuning of decoders to support the localization of new document classes. We conduct extensive experiments on a proprietary dataset of structured document images to demonstrate the effectiveness and the generalization capabilities of the proposed approach.

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Anastasiia Kabeshova, Guillaume Betmont, Julien Lerouge, Evgeny Stepankevich, Alexis Bergès. 2023-10-02. Data Efficient Training of a U-Net Based Architecture for Structured Documents Localization. https://arxiv.org/abs/2310.00937

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