DocParser: End-to-end OCR-free Information Extraction from Visually Rich Documents
This addresses the need for efficient and accurate document processing in commercial applications, offering a novel approach to bypass OCR bottlenecks.
The paper tackles the problem of information extraction from visually rich documents by proposing DocParser, an OCR-free end-to-end model that avoids dependence on external OCR systems. It achieves state-of-the-art results on various datasets and is faster than previous methods.
Information Extraction from visually rich documents is a challenging task that has gained a lot of attention in recent years due to its importance in several document-control based applications and its widespread commercial value. The majority of the research work conducted on this topic to date follow a two-step pipeline. First, they read the text using an off-the-shelf Optical Character Recognition (OCR) engine, then, they extract the fields of interest from the obtained text. The main drawback of these approaches is their dependence on an external OCR system, which can negatively impact both performance and computational speed. Recent OCR-free methods were proposed to address the previous issues. Inspired by their promising results, we propose in this 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 extract discriminative character features. DocParser achieves state-of-the-art results on various datasets, while still being faster than previous works.