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For digitizing or indexing physical documents, Optical Character Recognition(OCR), the process of extracting textual information from scanned documents, isa vital technology. When a document is visually damaged or contains non-textualelements, existing technologies can yield poor results, as erroneous detectionresults can greatly affect the quality of OCR. In this paper we present adetection network dubbed BusiNet aimed at OCR of business documents. Businessdocuments often include sensitive information and as such they cannot beuploaded to a cloud service for OCR. BusiNet was designed to be fast and lightso it could run locally preventing privacy issues. Furthermore, BusiNet isbuilt to handle scanned document corruption and noise using a specializedsynthetic dataset. The model is made robust to unseen noise by employingadversarial training strategies. We perform an evaluation on publicly availabledatasets demonstrating the usefulness and broad applicability of our model.

BusiNet -- a Light and Fast Text Detection Network for Business Documents