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Computer Science > Computer Vision and Pattern Recognition

arXiv:2205.08257 (cs)
[Submitted on 17 May 2022]

Title:Detection Masking for Improved OCR on Noisy Documents

Authors: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
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2205.08257 [cs.CV]
  (or arXiv:2205.08257v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2205.08257

Submission history

From: Daniel Rotman [view email]
[v1] Tue, 17 May 2022 11:59:18 UTC (2,170 KB)
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