> ## Documentation Index
> Fetch the complete documentation index at: https://hub.hcompany.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Document OCR
> Turn a document page image into clean Markdown with one chat completion.
export const Notice = ({kind = "note", title, children}) => {
const kinds = {
warning: {
label: "User notice",
icon: <>
},
gotcha: {
label: "Gotcha",
icon: <>
},
note: {
label: "Note",
icon: <>
}
};
const k = kinds[kind];
return
{k.label}
{title &&
{title}
}
{children}
;
};
Pass Holo a document page as an image and get clean Markdown back: headings, lists, tables, and equations, in reading order. There is no dedicated OCR endpoint: it is the same OpenAI-compatible `chat/completions` call with an image plus a transcription prompt, sent with `temperature=0.0` and `enable_thinking=False` so the model transcribes in one shot.
| Input | Quality |
| ----------------------------------------------------------------------- | --------------------------------------------------------------- |
| English, digitally generated: exported PDFs, slides, web pages, reports | Strongest |
| Scanned pages and photos | Best effort |
| Handwriting, non-Latin scripts | Not a good fit; use a dedicated OCR system when stakes are high |
Set up the OpenAI client first by following the [Quickstart](/models-api/quickstart).
## Transcribe a page
Send one page image and read the Markdown from `message.content`.
```python Python theme={"system"}
IMAGE_URL = "https://your-host/page.png" # or "data:image/png;base64,..."
OCR_PROMPT = (
"Transcribe this document page to Markdown, preserving the reading order, "
"headings, lists, and tables. Render tables as Markdown tables and equations "
"as LaTeX. Return only the transcription, with no commentary and no surrounding "
"code fence. If the page has no readable text, return an empty string."
)
response = client.chat.completions.create(
model="holo3-1-35b-a3b",
messages=[{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": IMAGE_URL}},
{"type": "text", "text": OCR_PROMPT},
],
}],
temperature=0.0,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
print(response.choices[0].message.content)
```
```typescript TypeScript theme={"system"}
const IMAGE_URL = "https://your-host/page.png"; // or "data:image/png;base64,..."
const OCR_PROMPT =
"Transcribe this document page to Markdown, preserving the reading order, " +
"headings, lists, and tables. Render tables as Markdown tables and equations " +
"as LaTeX. Return only the transcription, with no commentary and no surrounding " +
"code fence. If the page has no readable text, return an empty string.";
const response = await client.chat.completions.create({
model: "holo3-1-35b-a3b",
messages: [
{
role: "user",
content: [
{ type: "image_url", image_url: { url: IMAGE_URL } },
{ type: "text", text: OCR_PROMPT },
],
},
],
temperature: 0.0,
// chat_template_kwargs is H-specific, passed through in the request body
...({ chat_template_kwargs: { enable_thinking: false } } as any),
});
console.log(response.choices[0].message.content);
```
## Multi-page PDFs
Holo reads images, not PDFs, so rasterize each page to an image and transcribe them one per request, then stitch the results. One page per request keeps each image at full resolution and is the most reliable pattern.
```python Python theme={"system"}
import base64
import pymupdf # pip install pymupdf
def ocr_page(png_bytes: bytes) -> str:
data_uri = "data:image/png;base64," + base64.b64encode(png_bytes).decode()
response = client.chat.completions.create(
model="holo3-1-35b-a3b",
messages=[{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": data_uri}},
{"type": "text", "text": OCR_PROMPT},
],
}],
temperature=0.0,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
return response.choices[0].message.content or ""
with pymupdf.open("document.pdf") as doc:
pages = [ocr_page(page.get_pixmap(dpi=200).tobytes("png")) for page in doc]
markdown = "\n\n".join(pages)
print(markdown)
```
```typescript TypeScript theme={"system"}
import { pdf } from "pdf-to-img"; // npm install pdf-to-img
async function ocrPage(png: Buffer): Promise {
const dataUri = "data:image/png;base64," + png.toString("base64");
const response = await client.chat.completions.create({
model: "holo3-1-35b-a3b",
messages: [
{
role: "user",
content: [
{ type: "image_url", image_url: { url: dataUri } },
{ type: "text", text: OCR_PROMPT },
],
},
],
temperature: 0.0,
...({ chat_template_kwargs: { enable_thinking: false } } as any),
});
return response.choices[0].message.content ?? "";
}
const pages: string[] = [];
for await (const page of await pdf("document.pdf", { scale: 2 })) {
pages.push(await ocrPage(page));
}
const markdown = pages.join("\n\n");
console.log(markdown);
```
Rasterize at roughly 150 to 200 DPI (or `scale: 2`). Lower resolution loses small text. Higher resolution wastes tokens without improving accuracy. Run pages concurrently to speed up long documents, within your [rate limit](/models-api/introduction#faq).
Output is capped at 8,192 tokens per request, and a dense page (large tables, small print) can exceed that. Check `finish_reason` and treat `length` as a truncated transcription. Split dense pages into two images.
## Next steps
Get click coordinates from a screenshot.
How to use Holo in your computer-use harness.
Endpoint, models, parameters, and limits.