Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
+license: cc-by-4.0
 task_categories:
   - object-detection
   - image-to-text
@@ -16,6 +16,8 @@ tags:
   - synthetic-data
   - synthdog
   - hdf5
+  - nvidia
+  - nemotron
 pretty_name: OCR Synthetic Multilingual v1
 size_categories:
   - 10M<n<100M
@@ -25,9 +27,9 @@ size_categories:
 
 ## Overview
 
-Synthetically generated OCR training dataset for multilingual text detection and recognition, produced by the **SynthDoG** (Synthetic Document Generator) pipeline (v5 / text-effect).
+Large-scale synthetically generated OCR training dataset for multilingual text detection and recognition. The data was produced using a heavily modified and extended version of [SynthDoG](https://github.com/clovaai/donut/tree/master/synthdog) (Synthetic Document Generator), originally introduced in the [Donut](https://github.com/clovaai/donut) project by Kim et al.
 
-This dataset is designed for training end-to-end OCR models (detection + recognition) used in the **NeMo Retriever OCR** pipeline.
+This dataset was used to train [**Nemotron OCR v2**](https://huggingface.co/nvidia/nemotron-ocr-v2), a state-of-the-art multilingual OCR model that is part of the [NVIDIA NeMo Retriever](https://www.nvidia.com/en-us/ai-data-science/products/nemo/) collection.
 
 ## Languages
 
@@ -43,6 +45,10 @@ This dataset is designed for training end-to-end OCR models (detection + recogni
 
 > Numbers in parentheses are the number of `.h5` files per split.
 
+## Related Model
+
+This dataset was created to train the detection, recognition, and relational components of [**Nemotron OCR v2**](https://huggingface.co/nvidia/nemotron-ocr-v2). See the model card for architecture details, evaluation results, and usage instructions.
+
 ## Directory Layout
 
 ```
@@ -208,15 +214,31 @@ with h5py.File("en/train/train_000.h5", "r") as f:
 | Test           | 221,867 samples (77 files)           |
 | Validation     | 220,157 samples (77 files)           |
 
+## Acknowledgements
+
+The synthetic data generation pipeline is based on [SynthDoG](https://github.com/clovaai/donut/tree/master/synthdog) from the Donut project, with substantial modifications to support additional languages, custom rendering effects, structured bounding-box annotations (word/line/paragraph levels with reading-order graphs), and HDF5 output.
+
 ## Citation
 
-If you use this dataset, please cite:
+If you use this dataset, please cite both this dataset and the original SynthDoG work:
+
+```bibtex
+@misc{chesler2026ocr_synthetic_multilingual,
+  title   = {{OCR-Synthetic-Multilingual-v1}},
+  author  = {Chesler, Ryan},
+  year    = {2026},
+  publisher = {NVIDIA},
+  url     = {https://huggingface.co/datasets/nvidia/OCR-Synthetic-Multilingual-v1},
+  note    = {Synthetically generated multilingual OCR dataset built on a heavily modified SynthDoG pipeline}
+}
+```
 
 ```bibtex
-@misc{ocr_synthetic_multilingual_v1,
-  title={OCR-Synthetic-Multilingual-v1},
-  year={2025},
-  publisher={Hugging Face},
-  note={Synthetically generated multilingual OCR dataset produced by the SynthDoG pipeline (v5 / text-effect)}
+@inproceedings{kim2022donut,
+  title     = {{OCR-free Document Understanding Transformer}},
+  author    = {Kim, Geewook and Hong, Teakgyu and Yim, Moonbin and Nam, JeongYeon and Park, Jinyoung and Yim, Jinyeong and Hwang, Wonseok and Yun, Sangdoo and Han, Dongyoon and Park, Seunghyun},
+  booktitle = {European Conference on Computer Vision (ECCV)},
+  year      = {2022},
+  publisher = {Springer}
 }
 ```