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New protocol tests OCR alignment accuracy under reading-order errors

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new protocol to test the correctness of word-level alignment between Optical Character Recognition (OCR) output and ground truth, particularly when zoning and reading-order errors are present. The study revisits the "Zoning Problem" from a 2019 paper, introducing methods that evaluate performance using metrics like correct-match F1, cascade length, and recovery. Experiments involved synthetic data with various errors, historical newspaper data, and geometry-derived ground truth for Tesseract OCR, revealing that while local tracking can fail with large displacements, anchor-based methods handle many such cases but struggle when unique anchors are scarce. AI

IMPACT Improves accuracy in OCR systems, which are foundational for digitizing and processing text data in various AI applications.

RANK_REASON The item is an academic paper detailing a new protocol and experimental results for a specific problem in OCR post-processing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New protocol tests OCR alignment accuracy under reading-order errors

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The item is an academic paper detailing a new protocol and experimental results for a specific problem in OCR post-processing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Fonseca Cacho, Jorge Ram\'on ·

    Word-Level Alignment of OCR and Ground Truth under Zoning and Reading-Order Errors

    arXiv:2611.13752v1 Announce Type: new Abstract: OCR post-processing needs OCR text aligned word by word with its ground truth. Data sets rarely provide that mapping, and a monotone aligner can lose its place when zoning errors permute the reading order. We revisit this problem, t…

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