Bodhan September 2026
Collection
Collection of multilingual, multimodal models released as part of 5 Sep 2026 release • 5 items • Updated • 3
You need to agree to share your contact information to access this model
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
Please provide your details and agree to the LICENSE [simpler version] to request access.
Log in or Sign Up to review the conditions and access this model content.
Document parsing for English and 22 Indian languages, printed and handwritten. A page image in; reading-ordered Markdown out, with math as LaTeX and tables as HTML or Markdown, plus per-block JSON.
IndicOCR reads a document page and returns its text in reading order. It is a modular, two-stage parser: IndicDocLayout detects the blocks on the page and orders them, and IndicBlockOCR transcribes the textual blocks. The two stages communicate through a structured JSON file, so either stage can be used independently or replaced with another implementation.
ARCHITECTURE.md traces one page through the whole call path, names what
each module does, and lists the invariants that break the output silently when violated.
Examples
Detected blocks with their reading order on the left, the transcription on the right.
Example #1. English page with dense mathematics.
Example #2. Printed Telugu page.
Example #3. Handwritten Hindi maths.
Model Summary
| IndicDocLayout | IndicBlockOCR | |
|---|---|---|
| Role | Layout detection + reading order | Block-level text recognition |
| Architecture | PP-DocLayoutV3 / RT-DETR | Qwen3.5-0.8B |
| Parameters | 33 M | 0.8 B |
| Precision | fp32 | bf16 |
| In this repo | weights/layout (133 MB) |
weights/ocr (1.7 GB) |
| Output | Layout JSON | Markdown + block JSON |
IndicBlockOCR uses the Sarvam-30B tokenizer, with a vocabulary designed to cover Indian scripts. IndicDocLayout is a fine-tune of PP-DocLayoutV3/RT-DETR, trained with a 37-class taxonomy designed for education-domain documents.
IndicDocLayout predicts a labelled bounding box for each detected layout element. The 37 supported labels are:
Advertisement, Answer, Author, Chapter-end-section, Chapter-title, Chart, Code, Contact-info, Dateline, Diagram, Equation, Expression, Flag, Folio, Footer, Footnote, Header, Image, Image-caption, Index, Infobox, List, MCQ, Page-number, Paragraph, Placeholder-text, Question, Reference, Section-title, Solved-example, Sub-section-title, Sub-sub-section-title, Table, Table-caption, Table-of-contents, Title, Website-link
Supported languages
Printed page recognition is supported across English and the 22 constitutionally recognised Indian languages: Assamese, Bengali, Bodo, Dogri, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Odia, Punjabi, Sanskrit, Santali, Sindhi, Tamil, Telugu, Urdu.
Handwriting recognition currently supports English and 12 Indian languages: Hindi, Bengali, Telugu, Marathi, Tamil, Gujarati, Kannada, Malayalam, Odia, Punjabi, Assamese, and Urdu.
Handwriting quality is still a work in progress, particularly across different writing styles. We are working on improving recognition and extending support to additional languages.
Usage
Installation
The repo ships an installer that reads your driver and picks matching CUDA wheels. If you work in a virtual environment, please activate it first, as the installer installs into whichever Python is active.
IDP=$(python -c "from huggingface_hub import snapshot_download as d; print(d('bodhan-ai/indic-ocr'))")
cd "$IDP" && ./install.sh
It will use uv if that is available, and pip otherwise. Where running a shell script is not
convenient, TROUBLESHOOTING.md lists the two commands it runs.
Basic inference
import sys
from huggingface_hub import snapshot_download
repo = snapshot_download("bodhan-ai/indic-ocr")
sys.path.insert(0, repo) # the code ships in the repo
from indic_ocr import IndicOCR
parser = IndicOCR.from_pretrained(repo)
page = parser.parse("page.png")
print(page["markdown"]) # reading-ordered Markdown
page also carries the per-block detail, which you can save as follows:
import json
with open("page.json", "w", encoding="utf-8") as f:
json.dump(page, f, ensure_ascii=False, indent=2)
Running one stage at a time
To run the two stages separately:
from indic_ocr import IndicDocLayout, IndicBlockOCR
layout = IndicDocLayout(f"{repo}/weights/layout").detect("page.png")
page = IndicBlockOCR(f"{repo}/weights/ocr").run("page.png", layout)
run() takes a layout object, a dict, or the path to a layout JSON file.
Output
parser.parse("page.png") returns the page metadata and its blocks in reading order:
{
"image": "sample1.png",
"width": 800,
"height": 1273,
"blocks": [
{"order": 0, "label": "Header", "type": "PageHeader",
"bbox_xyxy": [345.6, 51.7, 437.1, 114.7], "conf": 0.6, "text": ""},
{"order": 1, "label": "Page-number", "type": "PageNumber",
"bbox_xyxy": [367.8, 78.9, 413.2, 107.4], "conf": 0.747, "text": "229"},
{"order": 2, "label": "Paragraph", "type": "Text",
"bbox_xyxy": [77.9, 121.3, 711.8, 199.4], "conf": 0.863,
"text": "Thus we see that, if we can prove that twice the L.H.S. of (30) ..."}
]
}
| field | meaning |
|---|---|
order |
reading-order rank, 0-based and gap-free |
label |
the raw IndicDocLayout class (37-class taxonomy) |
type |
coarse pipeline category: Text, Table, Equation, Title, ... |
bbox_xyxy |
pixel box [x0, y0, x1, y1] |
conf |
detection confidence |
text |
transcription; "" for blocks not sent to the recognizer |
Note: Figures, charts, advertisements, running headers, and footers are not sent through the recognizer
by default. They remain in the JSON with text: "", so you can see what was detected and where.
Page numbers and other margin text such as folios are transcribed.
Schemas
Machine-readable JSON Schema for each envelope, in schemas/:
| file | describes |
|---|---|
layout_output.schema.json |
The layout file: what IndicDocLayout writes and IndicBlockOCR reads. Blocks and reading order, before any text is read, so there is no text key at all. |
parse_output.schema.json |
The parsed page shown above. Every block now has text; "" means the block was detected but deliberately not sent to the recognizer. |
A layout from your own detector must use a label from the 37-class taxonomy, or declare type
explicitly. An unrecognised label is rejected rather than silently read as prose.
Table format
Tables come back as HTML by default. Choose the format when you construct the parser:
parser = IndicOCR.from_pretrained(repo) # HTML (default)
parser = IndicOCR.from_pretrained(repo, table_format="markdown") # Markdown
Performance
OmniDocBench 1.6 (english subset)
| OmniDocBench 1.6 (english subset) | Overall↑ | TextEdit↓ | FormulaCDM↑ | TableTEDS↑ | TableTEDS-S↑ | Read OrderEdit↓ |
|---|---|---|---|---|---|---|
| PaddleOCRVL-1.6 | 96.36 | 0.03 | 98.55 | 93.37 | 96.33 | 0.09 |
| Chandra OCR 2 | 93.11 | 0.04 | 96.93 | 86.07 | 90.34 | 0.09 |
| IndicOCR (ours) | 92.76 | 0.04 | 97.53 | 85.10 | 90.58 | 0.11 |
| GPT-5.6-sol | 92.46 | 0.04 | 95.42 | 85.87 | 90.98 | 0.10 |
| Gemini 3.1 Pro | 91.15 | 0.06 | 95.53 | 83.46 | 88.77 | 0.13 |
| Surya OCR 2 | 91.13 | 0.04 | 95.67 | 81.61 | 86.37 | 0.10 |
| Sarvam Vision | 90.08 | 0.04 | 97.62 | 76.82 | 82.01 | 0.10 |
| Gemma-4-31B | 86.71 | 0.09 | 89.48 | 79.79 | 85.19 | 0.19 |
| Nemotron Parse 2 | 79.12 | 0.159 | 78.94 | 74.32 | 81.09 | 0.29 |
olmOCR-Bench (english subset)
| OlmoOCRBench (english subset) | Overall↑ | arxiv_math↑ | baseline↑ | headers_footers↑ | long_tiny_text↑ | multi_column↑ | old_scans↑ | old_scans_math↑ | table_tests↑ |
|---|---|---|---|---|---|---|---|---|---|
| Chandra OCR 2 | 85.9 | 86.7 | 99.8 | 91.5 | 93.7 | 84.7 | 51 | 88.2 | 92.2 |
| Sarvam Vision | 84.3 | 86.5 | 99.6 | 96.3 | 91 | 82.2 | 49.8 | 81 | 88.3 |
| Gemini 3.1 Pro | 82.6 | 90.5 | 99 | 82.9 | 88.5 | 81.6 | 47 | 84.3 | 87.3 |
| IndicOCR (ours) | 82.2 | 83.2 | 99.4 | 92.9 | 89.8 | 76 | 48.3 | 77.7 | 90 |
| Surya OCR 2 | 81.4 | 82.5 | 99.8 | 92.9 | 79.9 | 85.1 | 42.8 | 84.3 | 84.2 |
| Gemma-4-31B | 80.4 | 79 | 99.4 | 92.9 | 89.8 | 80.5 | 45.8 | 73.8 | 82.2 |
| PaddleOCRVL-1.6 | 78.7 | 85.1 | 98.4 | 96.2 | 75.3 | 83.9 | 39 | 68.3 | 83 |
| GPT-5.6-sol | 78 | 79.3 | 93.9 | 95.4 | 87.8 | 77.4 | 43.7 | 64.6 | 82.2 |
| Nemotron Parse 2 | 68.2 | 64 | 96.7 | 90 | 79.6 | 72.8 | 31.9 | 28.6 | 81.8 |
IndicOCR-PR: printed accuracy by language (higher is better)
Word-level accuracy, reported as 100 x (1 - WER).
| Language | Sarvam Vision | IndicOCR (ours) | Gemini 3.1 Pro | Surya OCR 2 | Gemma-4-31B | Chandra OCR 2 |
|---|---|---|---|---|---|---|
| Overall | 86.6 | 86.2 | 80.4 | 67.9 | 66.3 | 64.2 |
| Assamese | 89.5 | 90.2 | 90.7 | 86.4 | 70.6 | 73.5 |
| Bodo | 91.0 | 86.5 | 91.0 | 55.6 | 68.1 | 46.6 |
| Bengali | 91.6 | 91.4 | 92.5 | 81.1 | 83.9 | 79.2 |
| Dogri | 85.8 | 81.7 | 83.7 | 60.5 | 64.4 | 55.8 |
| English | 96.6 | 97.0 | 97.7 | 93.8 | 97.2 | 91.3 |
| Gujarati | 91.6 | 91.7 | 92.8 | 79.6 | 81.6 | 73.0 |
| Hindi | 95.7 | 96.0 | 96.3 | 90.3 | 93.7 | 89.3 |
| Konkani | 93.6 | 93.7 | 93.5 | 90.5 | 76.9 | 85.5 |
| Kannada | 88.8 | 88.0 | 89.8 | 75.7 | 68.3 | 69.6 |
| Kashmiri | 43.3 | 52.2 | 38.1 | 23.4 | 19.9 | 17.6 |
| Malayalam | 90.6 | 89.9 | 90.6 | 76.5 | 72.0 | 68.3 |
| Manipuri | 81.9 | 83.8 | 0.8 | 0.1 | 0.1 | 0.0 |
| Marathi | 93.9 | 93.5 | 94.5 | 84.3 | 89.1 | 83.1 |
| Maithili | 86.7 | 83.0 | 86.7 | 67.6 | 76.3 | 66.1 |
| Nepali | 92.5 | 91.5 | 93.7 | 87.6 | 87.2 | 82.1 |
| Odia | 77.5 | 75.7 | 84.8 | 64.5 | 38.7 | 62.6 |
| Punjabi | 92.2 | 93.2 | 93.5 | 86.3 | 75.1 | 84.1 |
| Sanskrit | 82.0 | 76.2 | 83.7 | 57.8 | 60.8 | 55.8 |
| Sindhi | 89.2 | 87.1 | 86.3 | 80.5 | 74.5 | 71.4 |
| Santhali | 71.9 | 74.7 | 0.2 | 0.1 | 0.2 | 0.0 |
| Tamil | 94.2 | 91.3 | 94.4 | 79.9 | 83.3 | 79.0 |
| Telugu | 84.3 | 82.3 | 85.5 | 63.1 | 66.6 | 59.6 |
| Urdu | 87.1 | 85.9 | 88.0 | 76.4 | 76.6 | 74.4 |
IndicOCR-HW: handwriting accuracy by language (higher is better)
Word-level accuracy, reported as 100 x (1 - WER).
| Language | Gemini 3.1 Pro | IndicOCR (ours) | Sarvam Vision | Gemma-4-31B | Chandra OCR 2 | Surya OCR 2 |
|---|---|---|---|---|---|---|
| Overall | 72.0 | 66.7 | 55.4 | 33.9 | 24.7 | 23.0 |
| Assamese | 71.6 | 66.1 | 47.8 | 24.1 | 8.9 | 17.8 |
| Bengali | 74.8 | 71.3 | 58.3 | 35.1 | 6.6 | 10.0 |
| English | 84.4 | 80.7 | 77.7 | 78.5 | 78.2 | 72.7 |
| Gujarati | 60.0 | 55.9 | 39.2 | 23.7 | 11.8 | 11.5 |
| Hindi | 83.1 | 77.6 | 72.3 | 70.7 | 54.6 | 42.7 |
| Kannada | 73.8 | 69.6 | 57.7 | 17.2 | 11.5 | 13.2 |
| Malayalam | 63.9 | 60.5 | 45.6 | 16.0 | 15.7 | 11.8 |
| Marathi | 79.0 | 70.2 | 61.8 | 56.5 | 35.4 | 28.8 |
| Odia | 66.7 | 68.2 | 40.6 | 15.5 | 19.4 | 19.9 |
| Punjabi | 70.1 | 69.4 | 54.8 | 11.7 | 11.5 | 15.7 |
| Tamil | 80.5 | 76.8 | 60.5 | 33.4 | 18.8 | 16.8 |
| Telugu | 72.0 | 53.5 | 59.1 | 32.0 | 20.8 | 14.6 |
| Urdu | 54.4 | 46.4 | 44.4 | 25.6 | 27.6 | 22.6 |
Limitations
Reading order remains a challenge for complex, multi-column layouts. Handwriting recognition is also still being improved, particularly across different writing styles and writing characteristics.
We are also extending handwriting support to additional Indic languages.
Throughput
We benchmarked our end-to-end pipeline with vLLM on a single NVIDIA H100 80GB GPU using a diverse mix of documents (math, tables, handwritten notes, research papers, multi-column, and even complex newspaper layouts) from the OmniDocBench V1.6 benchmark set (official english subset).
| Concurrency | Pages / s | Output tokens / s | Median page (ms) | p95 page (ms) |
|---|---|---|---|---|
| 32 | 5.13 | 6,594 | 3,295 | 19,243 |
| 64 | 6.26 | 7,935 | 4,813 | 34,196 |
| 128 | 6.34 | 7,736 | 9,337 | 72,970 |
| 256 | 6.27 | 7,700 | 28,188 | 81,229 |
License
Released under Indic Open Model License v1.0.
The release incorporates components distributed under Apache 2.0, including PP-DocLayoutV3, Qwen3.5, and the Sarvam-30B tokenizer. See the repository license and the corresponding upstream licenses for the applicable terms and attribution requirements.
If you find the license difficult to understand, here is a plain-language guide to the Indic Open Model License.
Broad, no-cost access for research, government, nonprofit, and commercial use — with a few conditions attached.
This deed is a human-readable summary of the license, not a substitute for it. Where the two disagree, the full Indic Open Model License governs.
You're free to
No cost, no royalty, worldwide — for research, government, nonprofit, and commercial use, at any scale.
- ✅ Run it — for inference, in a product, in research, however you like.
- ✅ Change it — fine-tune, distill, quantize, merge, or otherwise build on it.
- ✅ Self-host it — power your own product or service with it, commercial or not.
- ✅ Share it — pass on copies of the model or your own version of it.
As long as you
Five conditions cover almost everything. The rest of the license is these, spelled out in legal detail.
1. Give credit
Wherever you ship the model or a derivative to anyone else, say where it came from — and don't strip out existing notices.
"Built with [Model Name] from Bodhan AI / AI4Bharat."
2. Pass it on the same way
If you give your fine-tuned or derived version to anyone else — hand it over, or run it as a service for them — it carries this exact license. You can't relicense it on different terms.
3. Ask before hosting it for others
Self-hosting is free. But if you're going to run it as an API or hosted service that other people or companies call directly, that needs Bodhan AI's written sign-off first — unless you're a nonprofit, government, or academic user, or you publicly release an equally capable open version within 90 days.
4. Don't use it to cause harm
No exceptions — not even for nonprofit or research use. That means no:
- child sexual abuse material, or content that sexualizes minors
- weapons development, including chemical, biological, radiological, or nuclear
- mass surveillance or social-scoring systems
- disinformation campaigns, including election manipulation
- automated decisions that affect someone's legal rights without human oversight
- deepfakes or voice clones of real people without their consent
- robocalls, auto-dialers, or voice-phishing scams
- AI companion products designed to simulate romance or foster emotional dependency
5. Talk to us if your product gets huge
If your own product built on this — not through hosting it for others, that's covered above — crosses either threshold, you'll need a separate commercial license. Doesn't apply to nonprofit, government, or academic users.
| Threshold | |
|---|---|
| 500M+ | monthly active users |
| or | |
| $250M+ | annual revenue |
Citation
@misc{indicocr2026,
title = {IndicOCR: Multilingual Document Parsing for English and 22 Indian Languages},
author = {Bodhan AI and AI4Bharat},
year = {2026},
url = {https://bodhan.ai/research/blogs/indic-ocr}
}
- Downloads last month
- -