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README.md

@@ -1,16 +1,39 @@
 ---
-license: cc-by-4.0
-task_categories:
-- visual-question-answering
-- image-to-text
+license: mit
+language:
+- en
 tags:
-- optical-character-recognition
-- visual-question-answering
-- robustness
 - watermark
-- numerical
-- multimodal
-pretty_name: FADE
+- ocr
+- image-processing
 size_categories:
 - 1K<n<10K
----
+---
+
+# FADE Watermark OCR Dataset
+
+## Overview
+This dataset contains watermarked images, their corresponding masks, the alpha values used for watermarking, and the actual text embedded (as a 9-digit number). It is designed to train and evaluate OCR models in the presence of watermarks.
+
+## Dataset Structure
+The dataset is stored in Parquet format, sharded by the `alpha` value. Because Parquet preserves the schema, Hugging Face automatically detects the columns and data types.
+
+### Data Fields
+
+| Column Name            | Data Type | Description |
+| :---                   | :---      | :--- |
+| `Image with watermark` | `binary`  | The raw binary bytes of the watermarked image (JPEG). When loaded with Hugging Face `datasets`, this can be cast to a PIL Image. |
+| `Masked image`         | `binary`  | The raw binary bytes of the corresponding mask image. |
+| `alpha value`          | `float`   | The blending factor used to apply the watermark (e.g., 0.05, 0.1, 0.5, 1.0). |
+| `Actual answer`        | `string`  | The ground truth 9-digit watermark text embedded in the image. |
+
+## Usage
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("deep9539/FADE-watermark-ocr")
+
+# View the structure
+print(dataset)
+```