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arXiv · 2205.08257

Detection Masking for Improved OCR on Noisy Documents

Daniel Rotman·Ophir Azulai·Inbar Shapira·Yevgeny Burshtein·Udi Barzelay
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Abstract

Optical Character Recognition (OCR), the task of extracting textual information from scanned documents is a vital and broadly used technology for digitizing and indexing physical documents. Existing technologies perform well for clean documents, but when the document is visually degraded, or when there are non-textual elements, OCR quality can be greatly impacted, specifically due to erroneous detections. In this paper we present an improved detection network with a masking system to improve the quality of OCR performed on documents. By filtering non-textual elements from the image we can utilize document-level OCR to incorporate contextual information to improve OCR results. We perform a unified evaluation on a publicly available dataset demonstrating the usefulness and broad applicability of our method. Additionally, we present and make publicly available our synthetic dataset with a unique hard-negative component specifically tuned to improve detection results, and evaluate the benefits that can be gained from its usage

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Daniel Rotman, Ophir Azulai, Inbar Shapira, Yevgeny Burshtein, Udi Barzelay. 2022-05-17. Detection Masking for Improved OCR on Noisy Documents. https://arxiv.org/abs/2205.08257

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