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OCR model struggles with section titles; user seeks CRF or simpler fix

By PulseAugur Editorial · [1 sources] ·

A user on r/MachineLearning is seeking advice on improving their OCR model's ability to correctly identify section titles in PDF documents. The current DeepSeek-OCR model sometimes mislabels titles as regular text, hindering the extraction of document structure. The user is considering a conditional random field (CRF) or a GNN approach to re-classify text blocks using features like indentation, alignment, and numbering patterns, but is also open to simpler heuristic solutions. AI

IMPACT Potential improvements in document structure extraction could enhance AI's ability to process and understand complex legal and regulatory texts.

RANK_REASON User seeking technical advice on a specific tooling problem.

Read on r/MachineLearning →

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

OCR model struggles with section titles; user seeks CRF or simpler fix

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0 / 100
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Tool
User seeking technical advice on a specific tooling problem.
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67 days old
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Present_Mention_2757 ·

    My OCR model mislabels section titles as body text. Is a CRF the right fix, or am I overcomplicating it? [P]

    <!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>I'm working on extracting the hierarchical structure of long PDF documents (legal/regulatory text, lots of numbered sections) and would like to gather some feedback on my approach before committing to it.</p> <p><strong>What I…

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