---
language:
- en
- as
- bn
- brx
- doi
- gu
- hi
- kn
- ks
- kok
- mai
- ml
- mni
- mr
- ne
- or
- pa
- sa
- sat
- sd
- ta
- te
- ur
tags:
- mlx
- ocr
- document-parsing
- layout-analysis
- reading-order
- indic
library_name: mlx-vlm
pipeline_tag: image-text-to-text
license: other
license_name: indic-open-model-license-1.0
license_link: LICENSE.md
base_model: bodhan-ai/indic-ocr
---

# IndicOCR (MLX)

Built with IndicOCR from Bodhan AI / AI4Bharat.

MLX conversion of [bodhan-ai/indic-ocr](https://huggingface.co/bodhan-ai/indic-ocr)
for document parsing on Apple Silicon. A page image becomes reading-ordered
Markdown and per-block JSON, with equations in LaTeX and tables in HTML by default.

This repository contains two models:

| Stage | Directory | Model | Weights |
|---|---|---|---|
| Layout detection and reading order | `weights/layout` | IndicDocLayout: PP-DocLayoutV3 fine-tune, 37 classes, about 33M parameters | float32, 133 MB |
| Block transcription | `weights/ocr` | IndicBlockOCR: Qwen3.5-0.8B fine-tune | BF16, 1.7 GB, not quantized |

## Usage

Use an [mlx-vlm](https://github.com/Blaizzy/mlx-vlm) checkout that includes
`indic_ocr` support for the standard `load_model` interface. From that checkout:

```sh
python -m pip install -e .
```

Replace `page.png` with a document image:

```python
from mlx_vlm.utils import get_model_path, load_model

with load_model(get_model_path("HashNuke/indic-ocr-mlx")) as parser:
    page = parser.parse("page.png")

print(page.markdown)
page.save("page.json")
```

The root config embeds both stage configurations, and a standard safetensors
index points to their existing weight files. No conversion or preparation step
is needed. The weight files are unchanged.

The pipeline detects and cleans the layout, resolves nested equations, and
transcribes eligible blocks using greedy decoding. Skipped regions remain in
the JSON with empty text. Closing the parser releases its model references.

`page.save` writes the image name, dimensions, and block records; Markdown is
available separately as `page.markdown`. Each block has a zero-based `order`,
`label`, `type`, pixel-coordinate `bbox_xyxy` (`[x0, y0, x1, y1]`), confidence,
and text.

## Load individual stages

The repository root is a two-stage wrapper, not a standalone OCR model.
Download the repository and pass its local stage directories to the loaders:

```python
from pathlib import Path

from huggingface_hub import snapshot_download
from mlx_vlm import load
from mlx_vlm.utils import load_model

root = Path(snapshot_download("HashNuke/indic-ocr-mlx"))
layout = load_model(root / "weights/layout")
layout.eval()
ocr, processor = load(str(root / "weights/ocr"))
```

Do not pass `HashNuke/indic-ocr-mlx/weights/ocr` as a repository ID.
For stage-specific examples, see the
[layout README](https://huggingface.co/HashNuke/indic-ocr-mlx/blob/main/weights/layout/README.md)
and [OCR README](https://huggingface.co/HashNuke/indic-ocr-mlx/blob/main/weights/ocr/README.md).

## Languages and validation

Upstream reports printed-text support for English and 22 Indian languages.
Handwriting support covers English and 12 Indian languages; it does not cover
every printed-text language. See the
[upstream language coverage](https://huggingface.co/bodhan-ai/indic-ocr#supported-languages)
for details.

Greedy BF16 OCR matched fresh PyTorch transcriptions on an English title, an
equation, and a Telugu line. Complete page parsing was checked on an English
paper and annotated Telugu/Hindi gallery panels. A controlled table produced
HTML with the expected rows and cell values. These are sample-level checks,
not accuracy measurements across all supported languages; the gallery panels
retain annotation text and handwriting quality varies.

## License

The source weights are distributed under the
[Indic Open Model License v1.0](https://huggingface.co/HashNuke/indic-ocr-mlx/blob/main/LICENSE.md).
This conversion does not change the upstream license.