mlsanityData to 14 Oct 2026

Unknown-box Approximation to Improve Optical Character Recognition Performance

arXiv 2105.07983 ↗First posted 17 May 2021cs.CVcited by 4

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Abstract (arXiv)

Optical character recognition (OCR) is a widely used pattern recognition application in numerous domains. There are several feature-rich, general-purpose OCR solutions available for consumers, which can provide moderate to excellent accuracy levels. However, accuracy can diminish with difficult and uncommon document domains. Preprocessing of document images can be used to minimize the effect of domain shift. In this paper, a novel approach is presented for creating a customized preprocessor for a given OCR engine. Unlike the previous OCR agnostic preprocessing techniques, the proposed approach approximates the gradient of a particular OCR engine to train a preprocessor module. Experiments with two datasets and two OCR engines show that the presented preprocessor is able to improve the accuracy of the OCR up to 46% from the baseline by applying pixel-level manipulations to the document image. The implementation of the proposed method and the enhanced public datasets are available for download.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 41 references that are in the map. First posted on arXiv 17 May 2021.

Builds on

11 of 41 references.

References · 11

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Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2024NAF-DPM: A Nonlinear Activation-Free Diffusion Probabilistic Model for Document Enhancementcited by 9
  2. 2024Confidence-Aware Document OCR Error Detectioncited by 2
  3. 2023Document Image Cleaning using Budget-Aware Black-Box Approximationcited by 0
  4. 2024Learning Instance-Specific Parameters of Black-Box Models Using Differentiable Surrogatescited by 0