Update README.md

README.md

@@ -1,3 +1,17 @@
+---
+license: mit
+task_categories:
+- image-to-text
+language:
+- en
+tags:
+- OCR
+- STR
+- scene text
+- synthetic data
+size_categories:
+- 10M<n<100M
+---
 # UnionST: A Strong Synthetic Engine for Scene Text Recognition
 
 Official data synthesis code of the paper *"What’s Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution"*.
@@ -14,7 +28,7 @@ Scene Text Recognition (STR) relies critically on large-scale, high-quality trai
 
 ## Dataset
 
-UnionST-S, UnionST-P, and UnionST-R datasets (each containing 5M samples) can be downloaded here.
+UnionST-S, UnionST-P, and UnionST-R datasets (each containing 5M samples) can be downloaded here. We use the lmdb file format adopted by the mainstream STR protocol.
 
 ## Training Model
 [OpenOCR](https://github.com/Topdu/OpenOCR)
@@ -62,6 +76,4 @@ THE SOFTWARE.
 
 ## Acknowledgements
 - We thank the [SynthText](https://github.com/ankush-me/SynthText), [SynthTIGER](https://github.com/clovaai/synthtiger), [SVTRv2](https://github.com/Topdu/OpenOCR/blob/main/docs/svtrv2.md) and [Union14M](https://github.com/Mountchicken/Union14M) for their open-source code/datasets.
-- Special thanks also go to the training framework: [OpenOCR](https://github.com/Topdu/OpenOCR).
-
-
+- Special thanks also go to the training framework: [OpenOCR](https://github.com/Topdu/OpenOCR).