I'm building a web app where users upload PDFs and I need to extract their text. Regular text-based PDFs work well, but scanned or image-based PDFs are more difficult. I'm currently using Tesseract with image preprocessing, but accuracy varies significantly on low-quality scans, and the output often needs cleanup. Tables, formatting, and document structure are also hard to preserve.
I'm considering Tesseract, PaddleOCR, and Google Vision OCR. Tesseract is free but seems less accurate, PaddleOCR requires more setup, and Google Vision appears more reliable but becomes paid after its free allowance. I'm working with a limited student-project budget but want something reliable enough for real users and scalable later.
What OCR stack would you recommend? Is PaddleOCR a worthwhile upgrade from Tesseract? How do you control cloud OCR costs, and what preprocessing or fallback strategies have worked well for improving accuracy and preserving document structure?
4 Answers
PaddleOCR is generally a significant improvement over Tesseract for real-world scans, especially when pages are slightly rotated, unevenly lit, or contain small text. It uses separate detection and recognition models, which gives it more flexibility. Tesseract can still be useful as a lightweight baseline, but I’d start with PaddleOCR if the setup and hosting environment support it.
A hybrid approach keeps costs manageable: run PaddleOCR locally first, inspect its confidence scores, and send only low-confidence pages to a paid OCR service. Cache results by document hash so the same file is never processed twice, and batch requests when possible. For many student projects, the free allowance may be enough, but putting a hard spending limit and monitoring usage in place is still important.
Using cloud OCR only for difficult pages sounds like a good compromise instead of sending every document to it.
Preprocessing can matter as much as the OCR engine. A practical pipeline is to render PDF pages at around 300 DPI, convert to grayscale, reduce noise, deskew the page, and then apply adaptive or Otsu thresholding when appropriate. Rendering at only 150 DPI often makes small text difficult for either engine. For tables, OCR alone usually won’t preserve the structure perfectly, so expect some post-processing of bounding boxes or extracted regions.
If preserving tables and layout is more important than plain text, consider a document-layout tool such as Docling alongside OCR. PaddleOCR is strong for text detection and recognition, but tables and complex formatting generally need an additional layout-analysis or post-processing step. A multimodal model can also help turn OCR output into cleaned text or Markdown, though it will add latency and ongoing API costs, so it’s better suited to selected pages or lower-volume workloads.
That seems to be the consensus, so I’m going to test PaddleOCR first and keep a cloud service as a fallback.