The easiest way to find which OCR solution works best for you: I ran DeepSeek-OCR, PaddleOCR-VL and Datalab Surya on the same 150 pages, from handwritten notes and rotated scans to dense tables and a filled tax form. Why? Because one overall score only tells you who's ahead on average, not where a parser breaks on documents like yours. So every page was checked with simple yes/no tests: is this line present, is this cell under the right headings, does A come before B. Here's what I found: - No single parser won on every type of page - DeepSeek-OCR was the best on tables at 87.2 - On handwritten notes, one parser passed 5/6 tests while the other two passed 0 - On a filled 1040 tax form, PaddleOCR-VL returned only the form's title The video below shows every page, what each parser returned, and why each test passed or failed. So the right pick depends on your documents, not on who has the best average. The tool I used is OmniParse Bench, and it's 100% open-source. It has 16,288 tests across 2,937 pages in 92 languages, each tagged by what makes a page hard, like handwriting, rotation, tiny text, forms and tables. Scoring is done by open code with no LLM judge. So you can plug in any OCR solution and see exactly where it holds up and where it breaks. GitHub repo: (don't forget to star 🌟)