Nanonets-OCR-s – OCR model that transforms documents into structured markdown
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Re: Nanonets-OCR-s – OCR model that transforms documents into structured markdown
#2Full disclaimer: I work at Nanonets
Excited to share Nanonets-OCR-s, a powerful and lightweight (3B) VLM model that converts documents into clean, structured Markdown. This model is trained to understand document structure and content context (like tables, equations, images, plots, watermarks, checkboxes, etc.). Key Features:
LaTeX Equation Recognition Converts inline and block-level math into properly formatted LaTeX, distinguishing between $...$ and $$...$$.
Image Descriptions for LLMs Describes embedded images using structured tags. Handles logos, charts, plots, and so on.
Signature Detection & Isolation Finds and tags signatures in scanned documents, outputting them in blocks.
Watermark Extraction Extracts watermark text and stores it within tag for traceability.
Smart Checkbox & Radio Button Handling Converts checkboxes to Unicode symbols like , , and for reliable parsing in downstream apps.
Complex Table Extraction Handles multi-row/column tables, preserving structure and outputting both Markdown and HTML formats.
Huggingface / GitHub / Try it out: https://huggingface.co/nanonets/Nanonets-OCR-s
Try it with Docext in Colab: https://github.com/NanoNets/docext/blob/main/PDF2MD_README.m...
Re: Nanonets-OCR-s – OCR model that transforms documents into structured markdown
#3I'm curious, how does it do with non-english texts? It's my understanding that LLM-based OCR solutions fall way behind traditional ones once you introduce other languages.
Re: Nanonets-OCR-s – OCR model that transforms documents into structured markdown
#4With models like these, when multilingual is not mentioned it will perform really bad on real life non-english pdfs.
Re: Nanonets-OCR-s – OCR model that transforms documents into structured markdown
#5[dead]
Re: Nanonets-OCR-s – OCR model that transforms documents into structured markdown
#6It's not open-source (nor open-weight): https://huggingface.co/nanonets/Nanonets-OCR-s/discussions/2
Re: Nanonets-OCR-s – OCR model that transforms documents into structured markdown
#7There are no benchmarks or accuracy measures on a hold out set?
Re: Nanonets-OCR-s – OCR model that transforms documents into structured markdown
#8It's not open-source (nor open-weight): https://huggingface.co/nanonets/Nanonets-OCR-s/discussions/2
Hi, author of the model here. It is an open-weight model, you can download it from here: https://huggingface.co/nanonets/Nanonets-OCR-s
Re: Nanonets-OCR-s – OCR model that transforms documents into structured markdown
#9There are no benchmarks or accuracy measures on a hold out set?
Hi, author of the model here..
We have a benchmark for evaluating VLM on document understanding tasks: https://idp-leaderboard.org/ . But unfortunately, it does not include image to markdown as a task. The problem with evaluating an image to markdown is that even if the order of two blocks are different, it can still be correct. Eg: if you have both seller info and buyer info side by side in the image one model can extract the seller info first, and another model can extract the buyer info first. Both model will be correct but depending on the ground truth if you do fuzzy matching one model will have higher accuracy than the other one.
Normally, a company will train and test on a dataset that is trained on the same type of annotation (either left block first or right block first), and all other models can get a low score on their benchmark because they are trained on the opposite order of annotations.
Re: Nanonets-OCR-s – OCR model that transforms documents into structured markdown
#10With models like these, when multilingual is not mentioned it will perform really bad on real life non-english pdfs.
The model was primarily trained on English documents, which is why English is listed as the main language. However, the training data did include a smaller proportion of Chinese and various European languages. Additionally, the base model (Qwen-2.5-VL-3B) is multilingual. Someone on Reddit mentioned it worked on Chinese: https://www.reddit.com/r/LocalLLaMA/comments/1l9p54x/comment...