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Information Extraction from visually rich documents is a challenging taskthat has gained a lot of attention in recent years due to its importance inseveral document-control based applications and its widespread commercialvalue. The majority of the research work conducted on this topic to date followa two-step pipeline. First, they read the text using an off-the-shelf OpticalCharacter Recognition (OCR) engine, then, they extract the fields of interestfrom the obtained text. The main drawback of these approaches is theirdependence on an external OCR system, which can negatively impact bothperformance and computational speed. Recent OCR-free methods were proposed toaddress the previous issues. Inspired by their promising results, we propose inthis paper an OCR-free end-to-end information extraction model named DocParser.It differs from prior end-to-end approaches by its ability to better extractdiscriminative character features. DocParser achieves state-of-the-art resultson various datasets, while still being faster than previous works.

DocParser: End-to-end OCR-free Information Extraction from Visually Rich Documents