BusiNet -- a Light and Fast Text Detection Network for Business Documents
Abstract
For digitizing or indexing physical documents, Optical Character Recognition (OCR), the process of extracting textual information from scanned documents, is a vital technology. When a document is visually damaged or contains non-textual elements, existing technologies can yield poor results, as erroneous detection results can greatly affect the quality of OCR. In this paper we present a detection network dubbed BusiNet aimed at OCR of business documents. Business documents often include sensitive information and as such they cannot be uploaded to a cloud service for OCR. BusiNet was designed to be fast and light so it could run locally preventing privacy issues. Furthermore, BusiNet is built to handle scanned document corruption and noise using a specialized synthetic dataset. The model is made robust to unseen noise by employing adversarial training strategies. We perform an evaluation on publicly available datasets demonstrating the usefulness and broad applicability of our model.
Cite
@article{arxiv.2207.01220,
title = {BusiNet -- a Light and Fast Text Detection Network for Business Documents},
author = {Oshri Naparstek and Ophir Azulai and Daniel Rotman and Yevgeny Burshtein and Peter Staar and Udi Barzelay},
journal= {arXiv preprint arXiv:2207.01220},
year = {2022}
}