Request access to Lipikar's UnifiedIndicOCR Model

This model is released under CC BY-NC-ND 4.0. Weights are made available for inference and evaluation purposes. Fine-tuning, further training, or creation of derivative models is not permitted under this license.

By requesting access, you confirm that you have read and agree to the CC BY-NC-ND 4.0 license terms, and that you will use this model for non-commercial inference/testing purposes only — not for fine-tuning, retraining, or building derivative models.

UnifiedIndicOCR (PARSeq)

Multilingual Indic OCR model based on PARSeq (permuted autoregressive sequence model), with a ViT-L encoder. This repo is self-contained for inference — only torch, torchvision, timm and Pillow are required, no pytorch_lightning/hydra/training-repo dependencies.

Repo layout

├── infer.py                 # run this: OCR on one or more images
├── requirements.txt         # torch, torchvision, timm, Pillow — nothing else
├── weights/
│   └── parseq_indic.pt      # model weights + architecture/charset config
├── samples/                 # a couple of sample images to try
│   ├── adhyaksh_new_1.png
│   └── bn_test_4.jpg
└── strhub/                  # trimmed copy of the original strhub package —
                              # just the PARSeq nn.Module + Tokenizer, no
                              # pytorch_lightning/hydra/nltk training code

Model

Architecture: PARSeq, ViT-L encoder — embed_dim=1024, enc_depth=24, enc_num_heads=16, 3-layer decoder with dec_num_heads=16. Matches the parseq-L model config combined with configs/10lang.yaml in the training repo (verified against the checkpoint's own saved hyperparameters).
Input: 32×128 (H×W) RGB crops, patch_size=[4, 8].
Charset: 985 characters (multilingual Indic + Latin + punctuation) + 3 special tokens ([E]/[B]/[P]) = 988 tokens. Note: this is the exact charset stored inside the source checkpoint's own hyperparameters, which has one extra character (ZWJ, U+200D) compared to the checked-in configs/10lang.yaml in the training repo — that config file is stale by one character, so the checkpoint's own charset is used here instead of re-deriving it from the yaml.
Weights: converted from the training repo's psmr/train-saves/old-a-final-pt.pt (355M params, fp32, ~1.4 GB).

Setup

conda create --name indicocr python=3.10 -y
conda activate indicocr
pip install -r requirements.txt

Run

# Uses the bundled sample images by default
python infer.py

# Or point it at your own images
python infer.py --images path/to/img1.png path/to/img2.jpg --device cuda

Output:

samples/adhyaksh_new_1.png: '...recognized text...'  (confidence: 0.98xx)
samples/bn_test_4.jpg: '...recognized text...'  (confidence: 0.97xx)

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support