--- title: Which OCR Solution Works Best for Your Documents? description: No single OCR model wins across every document type, a 150-page test of DeepSeek-OCR, PaddleOCR-VL and Datalab Surya found. Datalab scored highest overall at... canonical: https://daily.dev/posts/which-ocr-solution-works-best-for-your-documents--bp4w0ndxo twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Which OCR Solution Works Best for Your Documents? | daily.dev og:description: No single OCR model wins across every document type, a 150-page test of DeepSeek-OCR, PaddleOCR-VL and Datalab Surya found. Datalab scored highest overall at... og:url: https://daily.dev/posts/which-ocr-solution-works-best-for-your-documents--bp4w0ndxo og:image: https://api.daily.dev/og/posts/BP4W0NdXo.png og:image:alt: Which OCR Solution Works Best for Your Documents? og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Which OCR Solution Works Best for Your Documents? **[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 9 min read · 0 upvotes · 0 comments ## Summary No single OCR model wins across every document type, a 150-page test of DeepSeek-OCR, PaddleOCR-VL and Datalab Surya found. Datalab scored highest overall at 88.2, versus 77.5 for DeepSeek-OCR and 73.4 for PaddleOCR-VL. But DeepSeek-OCR led on tables at 87.2, and on a filled 1040 tax form PaddleOCR-VL returned only the title while Datalab passed all 8 tests. The tests ran on OmniParse Bench, an open benchmark with 16,288 pass/fail tests across 2,937 pages in 92 languages. The post also covers uzu, a Rust inference engine that hit 92.1 tokens/s on Qwen3.5 9B versus 22.0 for llama.cpp and 25.1 for MLX by generating several tokens per model pass. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Which OCR model is best for handwritten notes versus dense tables? Datalab Surya performed best on handwritten notes, passing 5 of 6 tests while DeepSeek-OCR and PaddleOCR-VL passed 0. For dense tables, DeepSeek-OCR led with a score of 87.2, ahead of Datalab at 78.9 and PaddleOCR-VL at 78.0. Overall average scores (Datalab 88.2, DeepSeek-OCR 77.5, PaddleOCR-VL 73.4) hide these per-document-type differences. _Developers picking an OCR model for specific document types can track comparisons like this on daily.dev._ ### How does the uzu inference engine get faster token generation than llama.cpp and MLX? uzu reached 92.1 tokens/s running Qwen3.5 9B on a base M5 MacBook Pro, versus 22.0 for llama.cpp and 25.1 for MLX. It produces multiple tokens per model pass instead of one: a small drafter (DFlash) proposes 16 candidate positions, a tiny adapter (Weaver, 56.7M params) orders them into a tree, and the 9B model verifies the whole tree in one pass, averaging 7.5 tokens per pass. _Developers evaluating local LLM inference speed can follow engines like uzu on daily.dev._ ### How many tests does the OmniParse Bench OCR benchmark include and how is it scored? OmniParse Bench includes 16,288 tests across 2,937 pages in 92 languages, drawn from 2,343 source documents. Each test is a pass/fail check on text, tables, or layout, tagged by difficulty factors like handwriting, rotation, or math, and a page where the parser call fails counts as a score of 0 so hard pages can't be dropped. _Teams choosing an OCR tool for their own documents can track open benchmarks like this on daily.dev._ ## Similar posts on daily.dev - [Comparing the Top Open-source OCR Solutions](https://daily.dev/posts/comparing-the-top-open-source-ocr-solutions-wsnaenr3a) · Daily Dose of Data Science \| Avi Chawla \| Substack · 0 upvotes · 0 comments - [OCR Showdown — Evaluating Three OCR Models](https://daily.dev/posts/ocr-showdown-evaluating-three-ocr-models-ch9qm0xde) · Medium · 0 upvotes · 0 comments - [I Spent May Evaluating Different Engines for OCR](https://daily.dev/posts/i-spent-may-evaluating-different-engines-for-ocr-ui4dxigk9) · Towards Data Science · 9 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#ai-agents](https://daily.dev/tags/ai-agents), [#ai-inference](https://daily.dev/tags/ai-inference) [View this post on daily.dev](https://daily.dev/posts/which-ocr-solution-works-best-for-your-documents--bp4w0ndxo) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Which OCR Solution Works Best for Your Documents?","url":"https://daily.dev/posts/which-ocr-solution-works-best-for-your-documents--bp4w0ndxo","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/which-ocr-solution-works-best-for-your-documents--bp4w0ndxo"},"datePublished":"2026-10-28T00:01:55.948Z","dateModified":"2026-10-28T03:57:41.359Z","description":"No single OCR model wins across every document type, a 150-page test of DeepSeek-OCR, PaddleOCR-VL and Datalab Surya found. 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