--- title: "I Spent May Evaluating Different Engines for OCR" url: https://daily.dev/posts/i-spent-may-evaluating-different-engines-for-ocr-ui4dxigk9 source_url: https://towardsdatascience.com/i-spent-may-evaluating-different-engines-for-ocr type: article source: "Towards Data Science" published: 2026-06-03T16:52:40.400Z updated: 2026-06-07T17:50:29.540Z tags: ["machine-learning", "llm", "vlm"] reading_time: 16 upvotes: 9 comments: 0 language: 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. # I Spent May Evaluating Different Engines for OCR **[Towards Data Science](https://daily.dev/sources/tds)** · 16 min read · 9 upvotes · 0 comments ## Summary A hands-on benchmark of 14 OCR engines tested against 93 documents of varying difficulty — from clean invoices to handwritten notes, financial tables, and old newspapers. Engines tested include Tesseract, Docling, Marker, GLM-OCR, PaddleOCR-VL, DeepSeek-OCR, MinerU 2.5, Mistral OCR, Qwen3-VL, Gemini Flash 3.1 Lite, Claude Sonnet 4.6, LlamaParse, and AWS Textract. Key findings: Gemini Flash was the best all-rounder, Tesseract remains unbeatable for clean high-volume docs, Mistral OCR is the cost-effective structured option, and specialized small models underperform on out-of-distribution documents. The core takeaway is that OCR is a routing problem — classify documents and match them to the right engine based on cost, accuracy, and failure tolerance rather than paying for expensive structured APIs across the board. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## 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 - [Mistral OCR 4: cheap, self-hosted document AI](https://daily.dev/posts/mistral-ocr-4-cheap-self-hosted-document-ai-1wizdbrck) · The Next Web · 1 upvotes · 0 comments - [Mistral OCR 3 Technical Review: SOTA Document Parsing at Commodity Pricing](https://daily.dev/posts/mistral-ocr-3-technical-review-sota-document-parsing-at-commodity-pricing-mnulkv51h) · PyImageSearch · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#vlm](https://daily.dev/tags/vlm) [View this post on daily.dev](https://daily.dev/posts/i-spent-may-evaluating-different-engines-for-ocr-ui4dxigk9)