Data Efficient Training of a U-Net Based Architecture for Structured
  Documents Localization
Anastasiia Kabeshova Guillaume Betmont Julien Lerouge Evgeny Stepankevich Alexis Bergès
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.