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--- |
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task_categories: |
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- image-classification |
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- image-segmentation |
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- image-to-text |
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tags: |
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- OCR |
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- Text-Image Pairs |
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size_categories: |
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- 10M<n<100M |
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--- |
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# Atlas PDF to Image Cluster Dataset |
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https://github.com/atlasunified/PDF-to-Image-Cluster |
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# Dataset Description |
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This dataset is a collection of text extracted from PDF files, originating from various online resources. The dataset was generated using a series of Python scripts forming a robust pipeline that automated the tasks of downloading, converting, and managing the data. |
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# Dataset Summary |
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``` |
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Bounding box: [[0.10698689956331878, 0.008733624454148471], [0.7336244541484717, 0.008733624454148471], [0.7336244541484717, 0.06986899563318777], [0.10698689956331878, 0.06986899563318777]], Text: the Simchas Bais |
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Bounding box: [[0.013100436681222707, 0.12663755458515283], [0.7314410480349345, 0.12663755458515283], [0.7314410480349345, 0.1965065502183406], [0.013100436681222707, 0.1965065502183406]], Text: they are engaged in |
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Bounding box: [[0.0, 0.2445414847161572], [0.7379912663755459, 0.23580786026200873], [0.7379912663755459, 0.31222707423580787], [0.0, 0.31877729257641924]], Text: hey could become |
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Bounding box: [[0.008733624454148471, 0.36026200873362446], [0.7336244541484717, 0.36026200873362446], [0.7336244541484717, 0.425764192139738], [0.008733624454148471, 0.425764192139738]], Text: evil inclination still |
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Bounding box: [[0.004366812227074236, 0.48034934497816595], [0.31004366812227074, 0.4847161572052402], [0.31004366812227074, 0.5567685589519651], [0.004366812227074236, 0.5502183406113537]], Text: certainly |
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Bounding box: [[0.36899563318777295, 0.4890829694323144], [0.5480349344978166, 0.4890829694323144], [0.5480349344978166, 0.5524017467248908], [0.36899563318777295, 0.5524017467248908]], Text: men |
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Bounding box: [[0.5851528384279476, 0.4781659388646288], [0.740174672489083, 0.4781659388646288], [0.740174672489083, 0.5524017467248908], [0.5851528384279476, 0.5524017467248908]], Text: and |
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Bounding box: [[0.008733624454148471, 0.6004366812227074], [0.7336244541484717, 0.6004366812227074], [0.7336244541484717, 0.6681222707423581], [0.008733624454148471, 0.6681222707423581]], Text: e in separate areas. |
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Bounding box: [[0.9454148471615721, 0.6157205240174672], [0.9978165938864629, 0.6157205240174672], [0.9978165938864629, 0.6877729257641921], [0.9454148471615721, 0.6877729257641921]], Text: T |
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Bounding box: [[0.9519650655021834, 0.7532751091703057], [0.9978165938864629, 0.7532751091703057], [0.9978165938864629, 0.8078602620087336], [0.9519650655021834, 0.8078602620087336]], Text: 0 |
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Bounding box: [[0.9475982532751092, 0.851528384279476], [0.9978165938864629, 0.851528384279476], [0.9978165938864629, 0.9235807860262009], [0.9475982532751092, 0.9235807860262009]], Text: fl\n |
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``` |
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``` |
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Bounding box: [[0.011570247933884297, 0.428099173553719], [0.9867768595041322, 0.428099173553719], [0.9867768595041322, 0.4677685950413223], [0.011570247933884297, 0.4677685950413223]], Text: tural person subiect to the reguirements laic |
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Bounding box: [[0.0049586776859504135, 0.5173553719008265], [0.9884297520661157, 0.5140495867768595], [0.9884297520661157, 0.5636363636363636], [0.0049586776859504135, 0.5669421487603306]], Text: priate, the provisions of sections 43 and 44; |
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Bounding box: [[0.009917355371900827, 0.6082644628099173], [0.9900826446280991, 0.6082644628099173], [0.9900826446280991, 0.6528925619834711], [0.009917355371900827, 0.6528925619834711]], Text: section 3. A person with no municipality of r |
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Bounding box: [[0.009917355371900827, 0.7041322314049587], [0.9917355371900827, 0.7041322314049587], [0.9917355371900827, 0.743801652892562], [0.009917355371900827, 0.743801652892562]], Text: ied by the authorities in their country of resi |
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Bounding box: [[0.0049586776859504135, 0.7917355371900826], [0.9917355371900827, 0.7950413223140496], [0.9917355371900827, 0.8396694214876033], [0.0049586776859504135, 0.8347107438016529]], Text: firearm or firearm component in question ir |
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``` |
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``` |
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Bounding box: [[0.19349005424954793, 0.5334538878842676], [0.7902350813743219, 0.5370705244122965], [0.7902350813743219, 0.5822784810126582], [0.19349005424954793, 0.5786618444846293]], Text: Generic Drug Description |
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Bounding box: [[0.19529837251356238, 0.6274864376130199], [0.9909584086799277, 0.6274864376130199], [0.9909584086799277, 0.6708860759493671], [0.19529837251356238, 0.6708860759493671]], Text: Carboxymethylcellulose Sodium ( |
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``` |
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# Supported Tasks and Use Cases |
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The primary use case of this dataset is to serve as training data for machine learning models that operate on text data. This may include, but is not limited to, text classification, information extraction, named entity recognition, and machine translation tasks. |
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# Dataset Creation |
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This dataset was generated through a multi-stage Python pipeline designed to handle the downloading, conversion, and management of large datasets. |
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# Data Fields |
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As the dataset contains text extracted from PDF files, the data fields primarily include the extracted text, alongside metadata about the source PDF, such as file size, number of pages, and bounding box information. |
