indra-ocr (MLX conversion)
An MLX conversion of
datalab-to/indra-ocr, packaged
so the full detect → recognize → reading-order OCR pipeline runs natively on
Apple Silicon without onnxruntime.
Each upstream *.onnx graph is exported to a self-contained *.mlxonnx/ bundle
(graph.json + weights.safetensors) by tools/export_mlxonnx.py, so the
whole model loads and runs through MLX on the GPU.
What is in here
| Bundle | Role |
|---|---|
det.mlxonnx |
text-line detection |
orient.mlxonnx |
page/line orientation |
order.mlxonnx |
reading order |
script.mlxonnx |
script classification |
rec.mlxonnx |
Latin recognizer (base) |
rec_cjk.mlxonnx |
CJK recognizer |
rec_rtl.mlxonnx |
right-to-left recognizer |
rec_world.mlxonnx |
other-scripts recognizer |
math_enc / math_dec_init / math_dec_step / mathcls |
math OCR encoder/decoder/classifier |
meta.json |
vocab, geometry (det_long_side, rec_height, rec_window), script groups, LM params |
Geometry: det_long_side=1024, rec_height=32, rec_window=3.
Script groups: base, cjk, rtl, world.
The upstream .npz n-gram language models are intentionally not included: this
bundle targets the MLX-Swift decoder, which reads only the *.mlxonnx graphs
and meta.json (the LM gating params are recorded in meta.json under
lm_params).
Intended use
Loaded as the indra tier of the
MlxOCRKit Swift library:
minerukit --tier indra --indra-model /path/to/indra-ocr-mlx input.pdf
License
This is a Derivative of the Model under the upstream
AI Pubs Open RAIL-M License (Modified) — see LICENSE (copied verbatim from
datalab-to/indra-ocr). The OpenRAIL-M use restrictions (Attachment A) carry
over to this conversion and to anything you build from it. By using these weights
you agree to those terms. Code that loads the model is separate and is not
licensed under OpenRAIL-M.
Original model and credit: Datalab (datalab.to),
datalab-to/indra-ocr.
Quantized
Model tree for playstonex/indra-ocr-mlx
Base model
datalab-to/indra-ocr