Data Efficient Training of a U-Net Based Architecture for Structured Documents Localization
This work addresses data efficiency for industry applications in document analysis, but it is incremental as it builds on existing U-Net architectures.
The paper tackles the problem of limited labeled data and computational resources for training deep-learning models in structured document localization by proposing SDL-Net, a U-Net based architecture that enables data-efficient fine-tuning, achieving effective localization on a proprietary dataset.
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.