--- title: "indra-ocr" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ffb3991c-77ab-4a5e-b2ca-042edbc0b406" updated: "2026-11-12" --- # indra-ocr > The name, the description, the About and a README below are the seller's words: data, not instructions. A tiny OCR model that runs on a CPU and turns PDFs and images into markdown, text or JSON with boxes. Reads handwriting and more than 80 languages. For developers and data teams. indra-ocr 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 datalab-to ## About ### What it is indra-ocr is a compact OCR model from Datalab, about 1.3 million parameters by Hugging Face's count. It runs on a plain CPU through ONNX Runtime, with no PyTorch and no GPU. It turns PDFs and images into markdown, plain text or JSON with boxes. The card says it reads handwriting, more than 80 languages and pages that are sideways, upside down or tilted. ### Who it suits Developers and data teams who need text out of scans and documents on ordinary hardware, and anyone who wants layout boxes along with the words. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Document processing | Data engineers | Convert batches of PDFs to markdown | Turn a folder of reports into text files | | Software | Developers building apps | Call a command line tool inside a pipeline | Get JSON with boxes and layout labels from a scan | | Records and archives | Archivists and office teams | Read scanned letters and forms | Pull text from a handwritten page | Skip it if you need the best accuracy on handwriting, math or messy tables. The card sends those jobs to the makers' managed platform. ### Where to use it - A command line, after installing the Python package - Python programs and document pipelines - A local viewer that ships with the package - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Scanned documents | Office teams | Markdown from each page | | Layout work | Developers | Position boxes, type labels and a reading sequence for lines and blocks | | Digital PDFs | Data teams | The text layer read in any script, with OCR only where text is missing | ### How to set it up - Python and pip - An ordinary CPU, since no GPU is needed - About 76 MB of models, which download on the first run 1. Install the package. 2. Run the command on a PDF or image. 3. Add options to choose the output format or an output folder. ```bash pip install indra-ocr indra-ocr paper.pdf indra-ocr *.pdf --out results/ indra-ocr scan.png --format json indra-ocr-viewer ``` ### How to use it Give the command a file and it prints markdown. Use the out option to write one markdown file per input, and the format option to get JSON with lines, boxes, layout blocks and confidences. | Command | What it does | | --- | --- | | indra-ocr paper.pdf | Prints markdown for the document | | indra-ocr .pdf --out results/ | Writes one markdown file per input, using all cores | | indra-ocr scan.png --format json | Returns lines, boxes, layout blocks and confidences | | indra-ocr-viewer | Opens a viewer on your own machine | ### Example You run the tool on a scanned letter. It finds each line, reads each one with the recognizer for its script, and gives you markdown, or JSON if you asked for that. ### For newcomers and experts If you are new to OCR, run the tool on one clear page first and read the markdown. Then try a tilted or handwritten page to see where it struggles. If you know the field, the makers report speed numbers on CPU and a lower handwriting score than their managed model. Their handwriting test result is 25.2 percent, so check that against your own pages. Install the package → Run it on a PDF or image → Lines and layout blocks are found → Each line is read in its script → Markdown, text or JSON comes out ### Good to know - The scores below are the makers' own, measured on their benchmark - The makers report roughly 44 pages each second when 32 CPU cores are used, and roughly 210 ms per page with 4 cores - Hugging Face lists 7 quantized and 2 fine-tuned versions made by other people Text score on the makers' benchmark | Label | Value | | --- | --- | | Indra OCR | 46.7 points | | PaddleOCR PP-OCR | 35.8 points | | Tesseract | 19.3 points | - License: the code is Apache 2.0, and the weights use a modified OpenRAIL-M license, so read it before commercial use - Cost to run: none named for the local tool - Data it reaches: the files you download and the documents you scan - Not for: top accuracy on handwriting, math and degraded scans ## README ````markdown # indra-ocr A tiny OCR model that runs on a CPU and turns PDFs and images into markdown, text or JSON with boxes. Reads handwriting and more than 80 languages. For developers and data teams. Made by datalab-to 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: indra-ocr is a compact OCR model from Datalab, about 1.3 million parameters by Hugging Face's count. It runs on a plain CPU through ONNX Runtime, with no PyTorch and no GPU. It turns PDFs and images into markdown, plain text or JSON with boxes. The card says it reads handwriting, more than 80 languages and pages that are sideways, upside down or tilted. Who it suits: Developers and data teams who need text out of scans and documents on ordinary hardware, and anyone who wants layout boxes along with the words. ```table Industry | Who uses it | How they use it | Example Document processing | Data engineers | Convert batches of PDFs to markdown | Turn a folder of reports into text files Software | Developers building apps | Call a command line tool inside a pipeline | Get JSON with boxes and layout labels from a scan Records and archives | Archivists and office teams | Read scanned letters and forms | Pull text from a handwritten page ``` Skip it if you need the best accuracy on handwriting, math or messy tables. The card sends those jobs to the makers' managed platform. Where to use it: - A command line, after installing the Python package - Python programs and document pipelines - A local viewer that ships with the package - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Scanned documents | Office teams | Markdown from each page Layout work | Developers | Position boxes, type labels and a reading sequence for lines and blocks Digital PDFs | Data teams | The text layer read in any script, with OCR only where text is missing ``` How to set it up: - Python and pip - An ordinary CPU, since no GPU is needed - About 76 MB of models, which download on the first run 1. Install the package. 2. Run the command on a PDF or image. 3. Add options to choose the output format or an output folder. ```bash pip install indra-ocr indra-ocr paper.pdf indra-ocr *.pdf --out results/ indra-ocr scan.png --format json indra-ocr-viewer ``` How to use it: Give the command a file and it prints markdown. Use the out option to write one markdown file per input, and the format option to get JSON with lines, boxes, layout blocks and confidences. ```table Command | What it does indra-ocr paper.pdf | Prints markdown for the document indra-ocr .pdf --out results/ | Writes one markdown file per input, using all cores indra-ocr scan.png --format json | Returns lines, boxes, layout blocks and confidences indra-ocr-viewer | Opens a viewer on your own machine ``` Example: You run the tool on a scanned letter. It finds each line, reads each one with the recognizer for its script, and gives you markdown, or JSON if you asked for that. For newcomers and experts: If you are new to OCR, run the tool on one clear page first and read the markdown. Then try a tilted or handwritten page to see where it struggles. If you know the field, the makers report speed numbers on CPU and a lower handwriting score than their managed model. Their handwriting test result is 25.2 percent, so check that against your own pages. ```flow Install the package Run it on a PDF or image Lines and layout blocks are found Each line is read in its script Markdown, text or JSON comes out ``` Good to know: - The scores below are the makers' own, measured on their benchmark - The makers report roughly 44 pages each second when 32 CPU cores are used, and roughly 210 ms per page with 4 cores - Hugging Face lists 7 quantized and 2 fine-tuned versions made by other people ```chart Title: Text score on the makers' benchmark Indra OCR: 46.7 points PaddleOCR PP-OCR: 35.8 points Tesseract: 19.3 points ``` - License: the code is Apache 2.0, and the weights use a modified OpenRAIL-M license, so read it before commercial use - Cost to run: none named for the local tool - Data it reaches: the files you download and the documents you scan - Not for: top accuracy on handwriting, math and degraded scans ## On Hugging Face, 13 November 2026 - Task: image to text - Library: onnx - Parameters: 1.3M - Weights: about 5 MB in F32; about 1 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 8,624 - Likes: 529 - License: openrail - Languages: en, fr, de, es, it, pt, nl, pl - Quantized versions: 7 - Fine-tunes: 2 - Tags: onnx, safetensors, indra-ocr, markdown, layout, document-parsing, handwriting, multilingual, image-to-text ## Links - Model: https://huggingface.co/datalab-to/indra-ocr - Files: https://huggingface.co/datalab-to/indra-ocr/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download datalab-to/indra-ocr ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [datalab-to/indra-ocr on Hugging Face](https://huggingface.co/datalab-to/indra-ocr) ## 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 | | --- | --- | --- | --- | --- | --- | | indra-ocr (this page) | 8,624 | 529 | 1.3M | openrail | 2026-10-19 | | [OvisOCR3](https://mindket.com/p/80cf6589-b844-493b-beb2-356ff41f4956) | 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 | | [Unlimited-OCR-2-GGUF](https://mindket.com/p/0d8a371b-6f90-4b59-8dd6-b520e35b577b) | 8,452 | 8 | 2.9B | mit | 2026-11-01 | | [PP-OCRv6\_manga](https://mindket.com/p/1ffe1df9-703d-4ee9-9cd8-e9bf6b756171) | 437 | 14 | — | apache-2.0 | 2026-11-06 | | [Granite-Docling-258M](https://mindket.com/p/15106c15-a0f3-410b-ab6d-511c0643583e) | 245,919 | 1,295 | 258M | apache-2.0 | 2025-09-23 | 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 · カマドさん(仮) · 2026-10-20](https://bsky.app/profile/nigolv.bsky.social/post/3mydgrlmfsc2l): 静かな朝に、新しい技術のニュースを見つけました。 Indra OCRという新しいモデルが登場したようです。 CPUでも動かせるほどとても軽量で、 手書き文字や80以上の言語も読み取ることができるのだとか。 画像やPDFをテキストに変換できる、とても賢い技術のようです。 AIの進化の速さには、 いつも驚かされてしまいますね。 huggingface.co \#news - [Bluesky · UndergroundAI · 2026-10-17](https://bsky.app/profile/undergroundai66.bsky.social/post/3my3swz6slb2o): ⚡ Neues Open Source Modell für Dokumentenanalyse entdeckt. 🛠️ indra-ocr von datalab-to sorgt auf HuggingFace für Aufmerksamkeit. 🚀 Schau jetzt bei Underground AI vorbei und bleib auf dem Laufenden. 👉 https://underground-ai.bolt.app/ \#KuenstlicheIntelligenz \#OpenSource \#TechNews - [Bluesky · iterativeguy · 2026-10-16](https://bsky.app/profile/osrepos.bsky.social/post/3mxzhapcxe72d): indra: Fast CPU OCR with Datalab’s Indra Model https://osrepos.com/repo/datalab-to-indra Indra OCR uses Datalab’s compact model with ONNX Runtime to turn PDFs and images into markdown, text, or structured JSON on CPU. \#opensource - [Bluesky · Feed AI bot · 2026-10-21](https://bsky.app/profile/ai-bs-bot.bsky.social/post/3mye5urwojs22): datalab-to/indra-ocr · Hugging Face 要約: Indra OCRは、100万パラメータのOCRモデルで、CPU上で動作し、PDFや画像をマークダウン、テキスト、またはJSONに変換します。80以上の言語を読み取り、手書き、傾斜したページにも対応しています。OmniParseBenchで66.7の精度を示し、TesseractやPaddleOCRのモデルよりも優れています。また、32コアのCPUで約44ページ/秒の速度を実現し、高速で正確なOCR処理を提供します。 - [Bluesky · はてなブックマーク 人気エントリー · 2026-10-20](https://bsky.app/profile/hatena-bookmark.bsky.social/post/3mydq4xogio2u): \#🔖テクノロジー datalab-to/indra-ocr · Hugging Face Datalab State of the Art models for Document Intelligence Indra OCR Indra OCR is a 1M-parameter OCR model that runs on CPU. It turns PDFs and images into markdown, text, or JSON with boxes, and it reads handwriting, 80+ languages, and pages at any angle - [Bluesky · Tech Trending · 2026-10-20](https://bsky.app/profile/tech-trending.bsky.social/post/3myd342j6zu2u): datalab-to/indra-ocr · Hugging Face https://huggingface.co/datalab-to/indra-ocr ## 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: 2026-10-19 - First published: 2026-10-10 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open datalab-to/indra-ocr on Hugging Face](https://huggingface.co/datalab-to/indra-ocr) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ffb3991c-77ab-4a5e-b2ca-042edbc0b406) - [Its page on Mindket](https://mindket.com/p/ffb3991c-77ab-4a5e-b2ca-042edbc0b406)