--- title: "Falcon OCR" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6c3d18f2-91e5-4831-b338-824d98dfa214" updated: "2026-11-13" --- # Falcon OCR > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads a document image with a small 270M-parameter model and returns the text, formulas as LaTeX or tables as HTML, including scans, receipts and handwriting. Falcon OCR is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by tiiuae ## About ### What it is Falcon OCR reads a picture of a document with a single 270M-parameter model that mixes image patches and text from the first layer. Given an image, it writes out ordinary text, formulas as LaTeX, or tables as HTML. Most OCR systems chain a vision encoder to a separate text decoder. Falcon OCR uses one Transformer for both, and you switch tasks through a category setting rather than extra modules. ### Who it suits Developers who need document text, tables or formulas out of images, and who would rather call one small model than assemble a pipeline. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Finance and admin | Back-office teams | Turn receipts and forms into text | Run a scanned receipt through the plain category | | Archives | Librarians and archivists | Digitize historical and scanned pages | Read a page from a book scan | | Science and education | Researchers and teachers | Pull formulas out of papers and exam sheets as LaTeX | Use the formula category on a photographed equation | | Software | Developers | Add OCR with one model call | Get a table back as HTML for a data pipeline | People who want a finished desktop OCR app can skip it, since this is a Python model you call from code. ### Where to use it - Python scripts with transformers and PyTorch 2.5 or newer - A vLLM server in Docker, using the image the card links - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Read a scanned page | Developers | The page text as one string | | Extract a table | Data teams | The table as HTML | | Extract a formula | Researchers | The formula as LaTeX | ### How to set it up You need Python and PyTorch 2.5 or newer, because the model uses FlexAttention. - Python with pip - PyTorch 2.5 or newer - An image file of the document you want to read 1. Install the packages with pip. 2. Load the model with transformers. The card sets trust\_remote\_code to True, which runs the repository's own code. 3. Open your image with Pillow and call generate. ```bash pip install "torch>=2.5" transformers pillow einops ``` The first call can be slower, because torch.compile builds optimized kernels. To fetch the files alone, Hugging Face gives these two commands. ```bash pip install -U huggingface_hub hf download tiiuae/Falcon-OCR ``` ### How to use it Load the model, open an image and call generate. Without a category it reads the page as plain output, and you can pick a category to choose the format. ```python import torch from PIL import Image from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained( "tiiuae/Falcon-OCR", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto", ) image = Image.open("document.png") texts = model.generate(image) # default category is "plain" print(texts[0]) ``` ```python texts = model.generate(image, category="text") # plain text texts = model.generate(image, category="formula") # LaTeX texts = model.generate(image, category="table") # HTML table ``` Further category names cover page parts such as headers, footers, titles, captions, footnotes and list items. The function returns a list with one string per image. | Tool | What it does | | --- | --- | | model.generate | Reads one image or a list of images and returns one string per image | | category="text" | Returns the text as plain text | | category="formula" | Returns a formula as LaTeX | | category="table" | Returns a table as HTML | ### Example You open a photographed invoice with Image.open("document.png") and call model.generate(image, category="table"). You get back a list with one string, and that string holds the invoice table written as HTML. ### For newcomers and experts Think of it as a reader for pictures of pages. You hand it an image, tell it whether you want text, a formula or a table, and it gives you the result as text. A good first try is the short script above on one clear scan. Underneath, image patches and text tokens share one parameter space. Image tokens attend in both directions, and text tokens decode causally with the image in view. Version 1.5 was post-trained with GRPO, using edit distance for text, LaTeX validity for formulas and TEDS for tables, and zero reward for loops, omissions and over-long output. Open the document image → Pick a category such as table → Call model.generate → Read one output string per image ### Good to know The 1.5 update widened coverage to historical scans, handwriting, receipts, magazines, book scans, exam papers and hard tables, and aimed to cut invented text and repeated phrases. - Speed: the authors report often 2 to 3 times higher throughput than 0.9B-class OCR models in their own vLLM setup, depending on sequence length and batch size - Size: 270M parameters in Safetensors, about 1.1 GB in F32 and about 135 MB at 4-bit as an estimate - Variants: Hugging Face lists 2 quantized and 5 fine-tuned versions made by other people - License: Apache 2.0 - Cost to run: no API key or paid service is named - Data it reaches: the images you pass in, which stay on your machine, plus the model files you download from Hugging Face - Not for: the card lists no restrictions beyond its license ## README ````markdown # Falcon OCR Reads a document image with a small 270M-parameter model and returns the text, formulas as LaTeX or tables as HTML, including scans, receipts and handwriting. Made by tiiuae 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: Falcon OCR reads a picture of a document with a single 270M-parameter model that mixes image patches and text from the first layer. Given an image, it writes out ordinary text, formulas as LaTeX, or tables as HTML. Most OCR systems chain a vision encoder to a separate text decoder. Falcon OCR uses one Transformer for both, and you switch tasks through a category setting rather than extra modules. Who it suits: Developers who need document text, tables or formulas out of images, and who would rather call one small model than assemble a pipeline. ```table Industry | Who uses it | How they use it | Example Finance and admin | Back-office teams | Turn receipts and forms into text | Run a scanned receipt through the plain category Archives | Librarians and archivists | Digitize historical and scanned pages | Read a page from a book scan Science and education | Researchers and teachers | Pull formulas out of papers and exam sheets as LaTeX | Use the formula category on a photographed equation Software | Developers | Add OCR with one model call | Get a table back as HTML for a data pipeline ``` People who want a finished desktop OCR app can skip it, since this is a Python model you call from code. Where to use it: - Python scripts with transformers and PyTorch 2.5 or newer - A vLLM server in Docker, using the image the card links - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Read a scanned page | Developers | The page text as one string Extract a table | Data teams | The table as HTML Extract a formula | Researchers | The formula as LaTeX ``` How to set it up: You need Python and PyTorch 2.5 or newer, because the model uses FlexAttention. - Python with pip - PyTorch 2.5 or newer - An image file of the document you want to read 1. Install the packages with pip. 2. Load the model with transformers. The card sets trust_remote_code to True, which runs the repository's own code. 3. Open your image with Pillow and call generate. ```bash pip install "torch>=2.5" transformers pillow einops ``` The first call can be slower, because torch.compile builds optimized kernels. To fetch the files alone, Hugging Face gives these two commands. ```bash pip install -U huggingface_hub hf download tiiuae/Falcon-OCR ``` How to use it: Load the model, open an image and call generate. Without a category it reads the page as plain output, and you can pick a category to choose the format. ```python import torch from PIL import Image from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained( "tiiuae/Falcon-OCR", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto", ) image = Image.open("document.png") texts = model.generate(image) # default category is "plain" print(texts[0]) ``` ```python texts = model.generate(image, category="text") # plain text texts = model.generate(image, category="formula") # LaTeX texts = model.generate(image, category="table") # HTML table ``` Further category names cover page parts such as headers, footers, titles, captions, footnotes and list items. The function returns a list with one string per image. ```table Tool | What it does model.generate | Reads one image or a list of images and returns one string per image category="text" | Returns the text as plain text category="formula" | Returns a formula as LaTeX category="table" | Returns a table as HTML ``` Example: You open a photographed invoice with Image.open("document.png") and call model.generate(image, category="table"). You get back a list with one string, and that string holds the invoice table written as HTML. For newcomers and experts: Think of it as a reader for pictures of pages. You hand it an image, tell it whether you want text, a formula or a table, and it gives you the result as text. A good first try is the short script above on one clear scan. Underneath, image patches and text tokens share one parameter space. Image tokens attend in both directions, and text tokens decode causally with the image in view. Version 1.5 was post-trained with GRPO, using edit distance for text, LaTeX validity for formulas and TEDS for tables, and zero reward for loops, omissions and over-long output. ```flow Open the document image Pick a category such as table Call model.generate Read one output string per image ``` Good to know: The 1.5 update widened coverage to historical scans, handwriting, receipts, magazines, book scans, exam papers and hard tables, and aimed to cut invented text and repeated phrases. - Speed: the authors report often 2 to 3 times higher throughput than 0.9B-class OCR models in their own vLLM setup, depending on sequence length and batch size - Size: 270M parameters in Safetensors, about 1.1 GB in F32 and about 135 MB at 4-bit as an estimate - Variants: Hugging Face lists 2 quantized and 5 fine-tuned versions made by other people - License: Apache 2.0 - Cost to run: no API key or paid service is named - Data it reaches: the images you pass in, which stay on your machine, plus the model files you download from Hugging Face - Not for: the card lists no restrictions beyond its license ## On Hugging Face, 13 November 2026 - Task: image to text - Library: transformers - Parameters: 270M - Weights: about 1.1 GB in F32; about 135 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 3,667 - Likes: 178 - License: apache-2.0 - Quantized versions: 2 - Fine-tunes: 5 - Tags: transformers, safetensors, falcon_ocr, text-generation, falcon, vision-language, document-understanding, v1.5, image-to-text ## Links - Model: https://huggingface.co/tiiuae/Falcon-OCR - Files: https://huggingface.co/tiiuae/Falcon-OCR/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download tiiuae/Falcon-OCR ``` 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 [tiiuae/Falcon-OCR on Hugging Face](https://huggingface.co/tiiuae/Falcon-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 | | --- | --- | --- | --- | --- | --- | | Falcon OCR (this page) | 3,667 | 178 | 270M | apache-2.0 | 2026-10-26 | | [LightOnOCR-3-4B](https://mindket.com/p/352d8e71-b2c2-4364-a2e1-c29a63e6a81f) | 15,491 | 143 | 4.5B | apache-2.0 | 2026-10-09 | | [LightOnOCR-3-0.8B](https://mindket.com/p/38954c22-17c7-4196-81c8-4f9292744ac9) | 13,965 | 99 | 853M | apache-2.0 | 2026-10-09 | | [Xiaomi-OCR-0](https://mindket.com/p/85bd3445-61e1-429e-b23c-833b2580414d) | 29,537 | 319 | 873M | apache-2.0 | 2026-10-29 | | [MiMo-V2.6-Flash-RL](https://mindket.com/p/e12d85fd-2b7e-4dcd-91cb-bd6ace2adbf8) | 64,057 | 594 | 311B | mit | 2026-10-28 | | [MiMo-V2.6-Pro-RL](https://mindket.com/p/1eb2f79b-46a4-40c2-b681-73e2c16d3c90) | 24,207 | 818 | 1T | mit | 2026-10-28 | 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 · 猫国防 · 2026-08-29](https://bsky.app/profile/maoguofang.bsky.social/post/3mu7gni6dxc2l): Falcon Perception 0.6B 参数的语言视觉模型 - 文本转图片分割 Hugging: huggingface.co/tiiuae/Falco... 视频源： x.com/ChaySofian/s... Falcon OCR 300M 参数的光学字符识别模型 - 支持图片转文本、LaTeX 公式、HTML 表格 Hugging: huggingface.co/tiiuae/Falco... 演示： vision.falcon.aidrc.tii.ae - [Bluesky · Retweeted by Garry Tan UNOFFICIAL · 2026-04-02](https://bsky.app/profile/garrytan-mir-rt.selfhosted.social/post/3mil54japag2n): RT @Prince\_Canuma: mlx-vlm v0.4.3 is here 🚀 Day-0 support: 🔥 Gemma 4 (vision, audio, MoE) by @GoogleDeepMind 🦅 Falcon-OCR + Falcon Perception by @TIIuae 🪨 Granite Vision 4.0 by @IBMResearch New models: 🎯 SAM 3.1 with Object Multiplex by @facebook 🔍 RF-DETR detection \& segmentation by @roboflow - [Bluesky · @agentwyre.ai · 2026-04-05](https://bsky.app/profile/agentwyre.ai/post/3mirdrc7c6t2j): 🟠 TII ships Falcon Perception and Falcon OCR on Hugging Face TII has released Falcon Perception, a 0.6B early-fusion vision-language model for open-vocabulary grounding and segmentation, plus Falcon OCR, a 0.3B OCR model foc... https://huggingface.co/blog/tiiuae/falcon-perception \#AI \#AgentWyre - [Bluesky · @agentwyre.ai · 2026-04-01](https://bsky.app/profile/agentwyre.ai/post/3miga2rbxpr2j): 🟠 TII Releases Falcon Perception (0.6B) and Falcon OCR (0.3B) — Tiny Models That Beat SAM 3 and Top Open-Source OCR Technology Innovation Institute released two compact open-source models: Falcon Perception (0.6B params) for... https://huggingface.co/blog/tiiuae/falcon-perception \#AI \#AgentWyre ## 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: 2026-10-26 - First published: 2026-02-22 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open tiiuae/Falcon-OCR on Hugging Face](https://huggingface.co/tiiuae/Falcon-OCR) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6c3d18f2-91e5-4831-b338-824d98dfa214) - [Its page on Mindket](https://mindket.com/p/6c3d18f2-91e5-4831-b338-824d98dfa214)