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 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 checkout that includes
indic_ocr support for the standard load_model interface. From that checkout:
python -m pip install -e .
Replace page.png with a document image:
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:
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
and OCR README.
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 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. This conversion does not change the upstream license.