Nanonets-OCR-s

Invoke Nanonets-OCR-s, an OCR model that reads a document image and returns its text as structured Markdown, with a 32k-token context length.

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View as Markdown Agent setup

Nanonets-OCR-s is an optical character recognition (OCR) model that converts a document image into structured Markdown. The output keeps the layout of the source, including headings, lists, tables, and basic tags, so another model can parse it. AI Inference runs it under the id nanonets/Nanonets-OCR-s.

Model details

The model id selects this model in a request, and Azion.AI.run takes it as the first argument.

DetailValue
Model nameNanonets-OCR-s
Model idnanonets/Nanonets-OCR-s

Capabilities

The values here describe the model itself, and none of them is a field in the request body. To look up the fields a request body accepts, with their types, defaults, and bounds, refer to Model invocation.

CapabilityValue
Input dataText and image
Context length32k tokens

Usage

Azion.AI.run runs the model from inside a function: the id goes in the first argument, and an OpenAI-compatible request body goes in the second. The example on this page calls it that way. To send the same body over HTTP instead, refer to Model invocation.

OCR

An OCR request carries the document image and the instruction that tells the model how to transcribe it. Both travel in one user message, as a content array whose parts each state a type. For the fields each part carries, refer to Message objects. This request reads a base64-encoded PNG and caps the answer at 500 tokens:

TypeScript
01const modelResponse = await Azion.AI.run("nanonets/Nanonets-OCR-s", {
02 "stream": false,
03 "max_tokens": 500,
04 "messages": [
05 {
06 "role": "user",
07 "content": [
08 {
09 "type": "image_url",
10 "image_url": {
11 "url": "data:image/png;base64,{img_base64}"
12 }
13 },
14 {
15 "type": "text",
16 "text": "Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation. If there is an image in the document and image caption is not present, add a small description of the image inside the <img></img> tag; otherwise, add the image caption inside <img></img>. Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>. Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number> or <page_number>9/22</page_number>. Prefer using ☐ and ☑ for check boxes."
17 }
18 ]
19 }
20 ]
21})

The model returns one entry in choices, and the transcribed text sits at choices[0].message.content:

JSON
01{
02 "id": "chatcmpl-0123456789abcdef0123456789abcdef",
03 "object": "chat.completion",
04 "created": 1767268800,
05 "model": "nanonets/Nanonets-OCR-s",
06 "choices": [
07 {
08 "index": 0,
09 "message": {
10 "role": "assistant",
11 "reasoning_content": null,
12 "content": "E = mc^2",
13 "tool_calls": []
14 },
15 "logprobs": null,
16 "finish_reason": "stop",
17 "stop_reason": null
18 }
19 ],
20 "usage": {
21 "prompt_tokens": 9,
22 "total_tokens": 527,
23 "completion_tokens": 518,
24 "prompt_tokens_details": null
25 },
26 "prompt_logprobs": null
27}
Model invocation

Every field a request body accepts, and the HTTP endpoint that takes the same body.

AI models

The other models AI Inference runs, and the id each one answers to.

AI Inference

The product that runs this model.

Functions

The product whose runtime holds the binding the example on this page calls.

AI Inference limits

The conditions under which Azion terminates or deprovisions a model.

Glossary

Where model id, context length, and the rest of the AI Inference vocabulary are defined.