Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-to-text
+---
+
+# UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters
+
+[[Paper](https://huggingface.co/papers/2512.21095)] [[Code](https://github.com/Topdu/OpenOCR)] [[ModelScope Demo](https://www.modelscope.cn/studios/topdktu/OpenOCR-UniRec-Demo)] [[Hugging Face Demo](https://huggingface.co/spaces/topdu/OpenOCR-UniRec-Demo)] [[Local Demo](#local-demo)]
+
+## Introduction
+
+**UniRec-0.1B** is a unified recognition model with only 0.1B parameters, designed for high-accuracy and efficient recognition of plain text (words, lines, paragraphs), mathematical formulas (single-line, multi-line), and mixed content in both Chinese and English. 
+
+It addresses structural variability and semantic entanglement by using a hierarchical supervision training strategy and a semantic-decoupled tokenizer. Despite its small size, it achieves performance comparable to or better than much larger vision-language models.
+
+## Get Started with the UniRec
+
+### Dependencies:
+
+- [PyTorch](http://pytorch.org/) version >= 1.13.0
+- Python version >= 3.7
+
+```shell
+conda create -n openocr python==3.10
+conda activate openocr
+# install gpu version torch >=1.13.0
+conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=11.8 -c pytorch -c nvidia
+# or cpu version
+conda install pytorch torchvision torchaudio cpuonly -c pytorch
+git clone https://github.com/Topdu/OpenOCR.git
+```
+
+### Downloding the UniRec Model from ModelScope or Hugging Face
+
+```shell
+cd OpenOCR
+pip install -r requirements.txt
+# download model from modelscope
+modelscope download topdktu/unirec-0.1b --local_dir ./unirec-0.1b
+# or download model from huggingface
+huggingface-cli download topdu/unirec-0.1b --local-dir ./unirec-0.1b
+```
+
+### Inference
+
+```shell
+python tools/infer_rec.py --c ./configs/rec/unirec/focalsvtr_ardecoder_unirec.yml --o Global.infer_img=/path/img_fold or /path/img_file
+```
+
+### Local Demo
+
+```shell
+pip install gradio==4.20.0
+python demo_unirec.py
+```
+
+### Training
+
+Additional dependencies:
+
+```shell
+pip install PyMuPDF
+pip install pdf2image
+pip install numpy==1.26.4
+pip install albumentations==1.4.24
+pip install transformers==4.49.0
+pip install -U flash-attn --no-build-isolation
+```
+
+It is recommended to organize your working directory as follows:
+
+```shell
+|-UniRec40M    # Main directory for UniRec40M dataset
+|-OpenOCR      # Directory for OpenOCR-related files
+|-evaluation   # Directory for evaluation dataset
+```
+
+Download the UniRec40M dataset from Hugging Face
+
+```shell
+# downloading small data for quickly training
+huggingface-cli download topdu/UniRec40M --include "hiertext_lmdb/**" --repo-type dataset --local-dir ./UniRec40M/
+huggingface-cli download topdu/OpenOCR-Data --include "evaluation/**" --repo-type dataset --local-dir ./
+```
+
+Run the following command to train the model quickly:
+
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_port=23333 --nproc_per_node=8 tools/train_rec.py --c configs/rec/unirec/focalsvtr_ardecoder_unirec.yml
+```
+
+To download the full dataset, you need to merge the split files named `data.mdb.part_*` (located in `HWDB2Train`, `ch_pdf_lmdb`, and `en_pdf_lmdb`) into a single `data.mdb` file. Execute the commands below step by step:
+
+```shell
+# downloading full data
+huggingface-cli download topdu/UniRec40M --repo-type dataset --local-dir ./UniRec40M/
+cd UniRec40M/HWDB2Train/image_lmdb & cat data.mdb.part_* > data.mdb
+cd UniRec40M/ch_pdf_lmdb & cat data.mdb.part_* > data.mdb
+cd UniRec40M/en_pdf_lmdb & cat data.mdb.part_* > data.mdb
+```
+
+And modify the `configs/rec/unirec/focalsvtr_ardecoder_unirec.yml` file as follows:
+
+```yaml
+...
+Train:
+  dataset:
+    name: NaSizeDataSet
+    divided_factor: &divided_factor [64, 64] # w, h
+    max_side: &max_side [960, 1408] # [64*30, 64*44] # w, h [960, 1408] #
+    root_path: path/to/UniRec40M
+    add_return: True
+    zoom_min_factor: 4
+    use_zoom: True
+    all_data: True
+    test_data: False
+    use_aug: True
+    use_linedata: True
+    transforms:
+      - UniRecLabelEncode: # Class handling label
+          max_text_length: *max_text_length
+          vlmocr: True
+          tokenizer_path: *vlm_ocr_config # path to tokenizer, e.g. 'vocab.json', 'merges.txt'
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  sampler:
+    name: NaSizeSampler
+    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
+    min_bs: 1
+    max_bs: 24
+  loader:
+    shuffle: True
+    batch_size_per_card: 64
+    drop_last: True
+    num_workers: 8
+...
+```
+
+## Citation
+
+If you find our method useful for your research, please cite:
+
+```bibtex
+@article{du2025unirec,
+  title={UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters},
+  author={Yongkun Du and Zhineng Chen and Yazhen Xie and Weikang Bai and Hao Feng and Wei Shi and Yuchen Su and Can Huang and Yu-Gang Jiang},
+  journal={arXiv preprint arXiv:2512.21095},
+  year={2025}
+}
+```