--- title: "OvisOCR3" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/80cf6589-b844-493b-beb2-356ff41f4956" updated: "2026-11-12" --- # OvisOCR3 > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads document images and returns text, structured JSON, Markdown with formulas and tables, key fields and figure captions from one 0.8B model. For developers who build OCR and document pipelines. OvisOCR3 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ATH-MaaS ## About ### What it is OvisOCR3 reads documents. It is a small model with 0.8B parameters that handles both pictures and text, and it starts from Qwen3.5-0.8B. One model covers document parsing, general OCR, key information extraction, figure captioning and cross-page table reconstruction. You pick the job by the prompt you give it, so you do not deploy a separate model for each task. ### Who it suits It suits developers and data teams who turn scanned pages, reports and forms into structured text and want one small model for the whole job. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Document processing | Data engineers | Convert page images into Markdown or structured JSON | Turning a scanned report into Markdown with tables | | Back-office work | Operations teams | Pull named fields from documents into key-value records | Filling a JSON template from a page image | | Publishing and research | Researchers | Capture formulas and figure captions from papers | Getting LaTeX for a formula on a page | | Data migration | Archive teams | Rebuild tables that run across consecutive pages | Joining a split table into one HTML table | You can skip it if you need a chat assistant or a model for tasks other than reading documents. ### Where to use it - Python projects that use the transformers library - The online demo space that the card links for a quick test - Windows, macOS or Linux with Python, to download the weights | Use case | Who | What they get back | | --- | --- | --- | | Parse a page | Developers | A structured JSON file, a layout JSON file, or Markdown that carries formulas and tables | | Extract fields | Analysts | Key-value records or JSON that follows your template | | Caption a figure | Researchers | Localized captions, with Mermaid for flowcharts | | Join split tables | Data teams | A complete HTML table with its source pages | ### How to set it up You need Python and a machine that can hold the weights, about 1.7 GB in BF16. - Python on Windows, macOS or Linux - The huggingface\_hub package - The transformers library to run the model - The task prompt files from the model repository 1. Install huggingface\_hub. 2. Download the model files to your computer. 3. Read the task prompts in the prompts folder, since they define each output format. 4. Load the model with transformers and send a page image with the prompt for your task. ```sh pip install -U huggingface_hub hf download ATH-MaaS/OvisOCR3 ``` ### How to use it You choose the task by the prompt. The card says its prompt files set the output contract, and its own examples use those files directly. The visible card text gives no run command beyond the download. ```sh hf download ATH-MaaS/OvisOCR3 ``` | Tool | What it does | | --- | --- | | Document parsing | Turns a page image into structured JSON, layout JSON, or Markdown | | General OCR | Reads an image and gives back text, box positions, formulas, chart values, a class label or an answer | | Key information extraction | Returns key-value records or JSON that follows your template | | Document figure captioning | Returns page content with localized captions | | Cross-page table reconstruction | Returns complete HTML tables from consecutive pages | ### Example You send a page image together with the parsing-mode prompt. The model returns the page as Markdown with LaTeX formulas and HTML tables, or as structured JSON, depending on the mode you chose. ### For newcomers and experts If you are new to OCR, think of this as a reader that looks at a page image and writes down what is on it. A first small try is one page image with the parsing prompt, then reading the Markdown it returns. For people who know the field, training ran in four steps: more pretraining, a middle phase, on-policy distillation, then reinforcement learning. In its own tables it leads most columns, though Xiaomi-OCR-0 scores slightly higher on OmniDocBench1.6, so check your own documents. OvisOCR3 document parsing scores | Label | Value | | --- | --- | | OmniDocBench1.6 | 96.74 | | MDPBench | 88.80 | | PureDocBench | 82.31 | | Real5-OmniDocBench | 93.63 | Page image and a task prompt go in → The 0.8B model reads and structures the page → Output is JSON, Markdown, HTML or text → You use the result in your pipeline ### Good to know The benchmark tables come from the model's authors and compare it against other OCR models of similar size. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the page images you feed it and the downloaded model files - Not for: chat or general text tasks, since the card describes OCR and document understanding ## README ````markdown # OvisOCR3 Reads document images and returns text, structured JSON, Markdown with formulas and tables, key fields and figure captions from one 0.8B model. For developers who build OCR and document pipelines. Made by ATH-MaaS on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: OvisOCR3 reads documents. It is a small model with 0.8B parameters that handles both pictures and text, and it starts from Qwen3.5-0.8B. One model covers document parsing, general OCR, key information extraction, figure captioning and cross-page table reconstruction. You pick the job by the prompt you give it, so you do not deploy a separate model for each task. Who it suits: It suits developers and data teams who turn scanned pages, reports and forms into structured text and want one small model for the whole job. ```table Industry | Who uses it | How they use it | Example Document processing | Data engineers | Convert page images into Markdown or structured JSON | Turning a scanned report into Markdown with tables Back-office work | Operations teams | Pull named fields from documents into key-value records | Filling a JSON template from a page image Publishing and research | Researchers | Capture formulas and figure captions from papers | Getting LaTeX for a formula on a page Data migration | Archive teams | Rebuild tables that run across consecutive pages | Joining a split table into one HTML table ``` You can skip it if you need a chat assistant or a model for tasks other than reading documents. Where to use it: - Python projects that use the transformers library - The online demo space that the card links for a quick test - Windows, macOS or Linux with Python, to download the weights ```table Use case | Who | What they get back Parse a page | Developers | A structured JSON file, a layout JSON file, or Markdown that carries formulas and tables Extract fields | Analysts | Key-value records or JSON that follows your template Caption a figure | Researchers | Localized captions, with Mermaid for flowcharts Join split tables | Data teams | A complete HTML table with its source pages ``` How to set it up: You need Python and a machine that can hold the weights, about 1.7 GB in BF16. - Python on Windows, macOS or Linux - The huggingface_hub package - The transformers library to run the model - The task prompt files from the model repository 1. Install huggingface_hub. 2. Download the model files to your computer. 3. Read the task prompts in the prompts folder, since they define each output format. 4. Load the model with transformers and send a page image with the prompt for your task. ```sh pip install -U huggingface_hub hf download ATH-MaaS/OvisOCR3 ``` How to use it: You choose the task by the prompt. The card says its prompt files set the output contract, and its own examples use those files directly. The visible card text gives no run command beyond the download. ```sh hf download ATH-MaaS/OvisOCR3 ``` ```table Tool | What it does Document parsing | Turns a page image into structured JSON, layout JSON, or Markdown General OCR | Reads an image and gives back text, box positions, formulas, chart values, a class label or an answer Key information extraction | Returns key-value records or JSON that follows your template Document figure captioning | Returns page content with localized captions Cross-page table reconstruction | Returns complete HTML tables from consecutive pages ``` Example: You send a page image together with the parsing-mode prompt. The model returns the page as Markdown with LaTeX formulas and HTML tables, or as structured JSON, depending on the mode you chose. For newcomers and experts: If you are new to OCR, think of this as a reader that looks at a page image and writes down what is on it. A first small try is one page image with the parsing prompt, then reading the Markdown it returns. For people who know the field, training ran in four steps: more pretraining, a middle phase, on-policy distillation, then reinforcement learning. In its own tables it leads most columns, though Xiaomi-OCR-0 scores slightly higher on OmniDocBench1.6, so check your own documents. ```chart Title: OvisOCR3 document parsing scores OmniDocBench1.6: 96.74 MDPBench: 88.80 PureDocBench: 82.31 Real5-OmniDocBench: 93.63 ``` ```flow Page image and a task prompt go in The 0.8B model reads and structures the page Output is JSON, Markdown, HTML or text You use the result in your pipeline ``` Good to know: The benchmark tables come from the model's authors and compare it against other OCR models of similar size. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the page images you feed it and the downloaded model files - Not for: chat or general text tasks, since the card describes OCR and document understanding ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 853M - Weights: about 1.7 GB in BF16; about 426 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 203,064 - Likes: 121 - License: apache-2.0 - Based on: Qwen/Qwen3.5-0.8B - Quantized versions: 7 - Fine-tunes: 1 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, document-parsing, multimodal, markdown, tables, formulas, key-information-extraction, figure-captioning, vllm ## Links - Model: https://huggingface.co/ATH-MaaS/OvisOCR3 - Files: https://huggingface.co/ATH-MaaS/OvisOCR3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ATH-MaaS/OvisOCR3 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [ATH-MaaS/OvisOCR3 on Hugging Face](https://huggingface.co/ATH-MaaS/OvisOCR3) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | OvisOCR3 (this page) | 203,064 | 121 | 853M | apache-2.0 | 2026-11-10 | | [OvisOCR2](https://mindket.com/p/49bb76fb-b54e-4ee4-b5aa-7971994ca65e) | 28,050 | 504 | 853M | apache-2.0 | 2026-10-22 | | [Xiaomi-OCR-0](https://mindket.com/p/85bd3445-61e1-429e-b23c-833b2580414d) | 29,537 | 319 | 873M | apache-2.0 | 2026-10-29 | | [Xiaomi-OCR-0-GGUF](https://mindket.com/p/1921d172-02df-42f8-b8be-4fa52ab31210) | 44,340 | 19 | 752M | apache-2.0 | 2026-10-30 | | [indra-ocr](https://mindket.com/p/ffb3991c-77ab-4a5e-b2ca-042edbc0b406) | 8,624 | 529 | 1.3M | openrail | 2026-10-19 | | [Qwopus3.8-27B-Coder-Flash](https://mindket.com/p/707a024a-e68e-4eb8-8e2a-54e878fe17ee) | 1,496 | 34 | 27.8B | apache-2.0 | 2026-11-07 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · AI Firehose · 2026-10-28](https://bsky.app/profile/ai-firehose.column.social/post/3myvn6uwvhw2t): OvisOCR3 from Alibaba's ATH-MaaS is a compact 0.8B model unifying OCR tasks and achieving high benchmark scores. Its method combines document parsing, information extraction, and table reconstruction, excelling in a size that boosts data processing across fields. https://arxiv.org/abs/2610.33066 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-10 - First published: 2026-10-21 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ATH-MaaS/OvisOCR3 on Hugging Face](https://huggingface.co/ATH-MaaS/OvisOCR3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/80cf6589-b844-493b-beb2-356ff41f4956) - [Its page on Mindket](https://mindket.com/p/80cf6589-b844-493b-beb2-356ff41f4956)