Title: Improving OCR accuracy for mixed-layout PDFs (Python): preprocessing & model suggestions? I’m building an automation tool that monitors a folder, detects new PDFs, runs OCR, and extracts specific fields into structured output. The pipeline uses Python with PyMuPDF, Tesseract/PaddleOCR, and Watchdog, plus some custom regex-based text extraction. I’m running into a few issues: Certain PDFs return very noisy OCR results — even though they look clean visually. Mixed layouts (tables, multi-column text) break extraction or produce jumbled text. Unsure which OCR model/setup provides the most reliable accuracy for such documents. Preprocessing uncertainty — what combination of deskewing, denoising, thresholding, etc. actually helps? Current setup: PyMuPDF for PDF → image conversion PaddleOCR (primary) + Tesseract fallback Regex-based extraction Folder auto-processing via Watchdog/PollingObserver I’m looking for suggestions on: Effective preprocessing pipelines in Python before OCR Best OCR engines/models for mixed structured documents Tools like LayoutParser / DocTR / Donut for layout-aware extraction Any ways to boost accuracy before text parsing If anyone has practical experience or benchmarks for these scenarios, I’d appreciate your insights. Thanks! Continue reading...
