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