Nanonets-OCR-s: More Than Just OCR! Open-Source Model Converts Images into Perfect Markdown with LaTeX and Table Support
Introducing Nanonets-OCR-s, a powerful open-source OCR model that accurately converts document images into structured Markdown. It handles everything from complex LaTeX equations to tables, signatures, and watermarks—seamlessly. A must-have for developers and researchers!
If you’re a developer, researcher, or anyone who works with a large number of documents, you’ve probably faced this frustrating scenario: staring at a scanned PDF or image file packed with important content, but having no choice but to retype it manually—one word at a time. Traditional OCR (Optical Character Recognition) tools might help a little, but often the output is a mess: formatting breaks down completely, and tables or math formulas become unreadable disasters.
We feel your pain—really.
But what if there was a tool that not only recognized the text, but also understood the structure and context of a document? That’s exactly what we’re introducing today: Nanonets-OCR-s, a game-changing open-source model.
This powerful yet lightweight (3B) Vision Language Model (VLM) is purpose-built to convert complex document images into clean, organized, and structured Markdown. Yes, it understands tables, parses mathematical equations, and even detects fine-grained details like signatures and checkboxes.
Not Just Text—It Understands Math Too (LaTeX Recognition)
Still copying math formulas from research papers by hand? Those days are over.
One of Nanonets-OCR-s’s standout features is its remarkable accuracy in recognizing LaTeX math expressions. It intelligently distinguishes between inline-level math and block-level equations and outputs them in standard Markdown syntax using $...$ and $$...$$.
This means whether you’re working on a physics paper, academic report, or engineering notes, you can instantly convert complex formulas and paste them directly into your Markdown editor or research notes—perfectly formatted.
Images Inside Images? No Problem—It Describes Them Too
Most OCR tools ignore embedded visual elements like charts, logos, or images in documents. Nanonets-OCR-s does more—it describes them using structured <img> tags.
Imagine passing this Markdown output to a large language model (LLM) for further processing. Thanks to these embedded image descriptions, the LLM can understand things like “this is a company logo” or “this is a bar chart showing sales trends.” This opens new possibilities for automated document summarization, analytics, and report generation.
A Savior for Contracts: Signature Detection
When handling contracts or official documents, signatures are critical. In the past, we had to manually screenshot or annotate them.
Now, Nanonets-OCR-s can automatically detect signatures in scanned documents and isolate them in a dedicated <signature> block. This not only makes the digitization process more complete but also simplifies archiving and validation.
Don’t Miss a Thing: Watermark Extraction
Official or draft documents often contain watermarks such as “Confidential” or “Draft” that indicate their status or origin. Though not part of the main content, these markings are important.
Nanonets-OCR-s can accurately extract watermark text and store it in a <watermark> tag. This is essential for ensuring document traceability and integrity and helps prevent misunderstandings due to overlooked watermarks.
Simplifies Surveys and Forms: Smart Checkbox Recognition
It might seem like a small detail, but for anyone working with surveys, forms, or checklists, this is a game-changer. Nanonets-OCR-s recognizes checkboxes and radio buttons in documents and converts them into standard Unicode symbols, such as:
- Checked: ☑
- Crossed: ☒
- Unchecked: ☐
This allows downstream applications (like data analysis tools) to reliably interpret these options—no more errors or messy formatting.
The Toughest Challenge: Tables—Handled with Precision
Tables are notoriously hard for OCR systems. Multi-row, multi-column tables often break traditional tools, leaving behind a jumble of unusable text.
Nanonets-OCR-s tackles this head-on. It can process structurally complex tables and accurately preserve their row and column formatting. Better yet, it outputs them in both Markdown and HTML. Whether you’re using them in notes or publishing them online, you’re good to go.
Intrigued? Try It Out Now!
Nanonets-OCR-s isn’t just a tool—it’s a powerful building block that can be seamlessly integrated into your document automation pipeline. Best of all? It’s completely open source.
We invite you to experience it for yourself:
- Hugging Face Model Page: Explore the model here
- Official Announcement: Read the full technical breakdown
- Try It on Colab: Hands-on with Docext on Colab
Frequently Asked Questions (FAQ)
Q1: How is Nanonets-OCR-s different from other OCR tools?
The key difference is structural understanding. Traditional OCR focuses on recognizing characters, while Nanonets-OCR-s understands the entire structure of a document—including paragraphs, headings, tables, formulas, and signatures. This makes the Markdown output not just readable, but directly usable for automation, far beyond what typical OCR tools offer.
Q2: Is this model free to use?
Yes! Nanonets-OCR-s is an open-source model. You can download and use it for free from Hugging Face, and integrate it into your own projects under its open-source license.
Q3: What does “lightweight (3B)” mean? What’s the benefit?
“3B” means the model has 3 billion parameters. Compared to the 10s or 100s of billions in many modern models, this is considered lightweight. It requires less hardware and can run on personal machines or standard servers without the need for high-end, expensive GPUs.
Q4: I’m not a developer—can I still use it?
While deploying the model requires some technical know-how, you can easily try it out using the official Colab notebook. Just upload your document image and see the converted Markdown output—it’s very user-friendly.
