Update README.md

README.md

@@ -1,3 +1,63 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- image-to-text
+language:
+- en
+tags:
+- document
+- image
+- open-pdf
+- 250+
+size_categories:
+- n<1K
+---
+# Openpdf-Blank-v2.0-Sample
+
+**Openpdf-Blank-v2.0-Sample** is a sample dataset of blank or near-blank invoice and receipt documents. It contains 255 high-resolution scanned images extracted and cleaned from document PDFs. This dataset is intended to support training and evaluation of OCR, document classification, and layout-based filtering models where blank or structurally minimal pages must be identified and processed.
+
+## Dataset Summary
+
+* **Format**: Parquet (auto-converted)
+* **Modality**: Image
+* **Size**: 84.8 MB
+* **Number of Samples**: 255
+* **Split**:
+
+  * `train`: 255 images
+* **Image Dimensions**: Approximately 1690 x 1690 px
+* **License**: Apache 2.0
+
+## Features
+
+* Contains scanned images of documents with minimal content or structural layout only.
+* Suitable for:
+
+  * Blank page detection
+  * Document filtering
+  * Pre-processing pipeline validation
+  * Background noise training for OCR tasks
+
+## How to Use
+
+You can load the dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("prithivMLmods/Openpdf-Blank-v2.0-Sample")
+
+# Access the first image
+image = dataset["train"][0]["image"]
+image.show()
+```
+
+Each record in the dataset contains:
+
+* `image`: A PIL.Image object of the scanned blank/near-blank page.
+
+## Use Cases
+
+* Training models to detect and discard blank or non-informative pages in document workflows.
+* Evaluating the robustness of OCR pipelines to blank document noise.
+* Dataset balancing for invoice or receipt classifiers.