Reddit user seeks feedback on OCR labeling approach for PDFs

A Reddit user in the r/MachineLearning community posted a request for feedback on their approach to extracting document structure from long PDF files. They are working with legal and regulatory documents that have complex, numbered hierarchical sections. Their pipeline renders each PDF page as an image and passes it through Baidu's DeepSeek-OCR model, which returns detected text blocks along with metadata: bounding boxes with coordinates [x0, y0, x1, y1], a semantic label (title, text, list, table, header, footer, and others), and the recognized text. The user notes that the OCR's text recognition quality is genuinely good—the extracted text comes out clean and accurate. However, the labels assigned by the model cannot always be trusted. Specifically, when the user tries to extract and identify all the titles in the document, some title elements are occasionally misclassified as body text or other label categories. The user provided a concrete example showing a document hierarchy: "ANNEX I — GENERAL PRINCIPLES AND PROCEDURES" contains "TITLE I — FOREIGN CURRENCY INVESTMENT," which contains "A. Currency distribution," which contains "1. Redistribution of reserves," followed by subsections labeled "(a) Introduction" and body text. The user is uncertain whether a Conditional Random Field—a machine learning model designed to predict sequences of labels while accounting for dependencies between adjacent labels—is the appropriate solution or whether they are overengineering the fix. They are seeking community feedback before committing to an approach.

The user is facing a common downstream problem in document processing: the quality of text recognition does not guarantee the quality of structural labeling. Baidu's DeepSeek-OCR delivers clean text extraction, but its classifier for block types (title vs. body vs. list vs. table, etc.) is imperfect in this domain. The user is working with heavily structured documents—legal and regulatory texts with numbered sections and hierarchical nesting—where structural integrity is as important as text accuracy. The question of whether to apply a Conditional Random Field (a probabilistic graphical model that can learn sequential dependencies between labels) or use a simpler heuristic-based or rule-based approach hinges on the trade-off between model complexity and the actual error rate in their specific corpus. The post is seeking community input to validate the approach before investing engineering effort.

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