Improving OCR Accuracy Through Multi-Classifier Verification of Visually Similar Characters
We present the Character Verification Model (CVM), a character-level post-processing framework that verifies and corrects ambiguous characters in OCR outputs using specialized binary classifiers trained on visually confusable pairs such as 'O'/'0', 'Z'/'2', and 'S'/'5'.
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Key points
- Distinguishing visually similar characters is a persistent challenge for optical character recognition (OCR) systems, causing errors in critical applications of text digitization.
- While preprocessing techniques improve image quality and word-level post-processing corrects spelling errors, neither addresses character-level visual ambiguity, an intrinsic property of certain character pairs that limits even state-of-the-art OCR models.
- Experiments on three OCR engines (Tesseract, EasyOCR, TrOCR) and ten fonts with low to high complexity demonstrate accuracy improvements of 3.95-14.81 percentage points, with an 87.3% correction rate for visually similar character errors and a false-correction rate of only 2.3%.
- The modular architecture integrates with existing OCR pipelines without modification to underlying recognition models.
Sources (1)
- [1]Improving OCR Accuracy Through Multi-Classifier Verification of Visually Similar CharactersarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 10:56 PM
We present the Character Verification Model (CVM), a character-level post-processing framework that verifies and corrects ambiguous characters in OCR outputs using specialized binary classifiers trained on visually confusable pairs such as 'O'/'0', 'Z'/'2', and 'S'/'5'.
Distinguishing visually similar characters is a persistent challenge for optical character recognition (OCR) systems, causing errors in critical applications of text digitization.
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