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--- |
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language: |
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- ar |
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pretty_name: "EvArEST dataset for Arabic scene text recognition" |
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tags: |
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- scene_text_detection |
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- scene_text_ocr |
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dataset_info: |
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features: |
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- name: image_bytes |
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dtype: image |
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- name: image_name |
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dtype: string |
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- name: image_width |
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dtype: int64 |
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- name: image_height |
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dtype: int64 |
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- name: full_text |
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dtype: string |
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splits: |
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- name: train_real |
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num_examples: 5454 |
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- name: train |
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num_examples: 208772 |
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- name: test |
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num_examples: 2649 |
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license: bsd-3-clause |
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--- |
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# EvArEST |
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Everyday Arabic-English Scene Text dataset, from the paper: [Arabic Scene Text Recognition in the Deep Learning Era: Analysis on A Novel Dataset](https://ieeexplore.ieee.org/abstract/document/9499028) |
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The dataset includes both the recognition dataset and the synthetic one in a single train and test split. |
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### Recognition Dataset |
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The text recognition dataset comprises of 7232 cropped word images of both Arabic and English languages. The groundtruth for the recognition dataset is provided by a text file with each line containing the image file name and the text in the image. The dataset could be used for Arabic text recognition only and could be used for bilingual text recognition. |
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Training Data: |
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[Arabic](https://drive.google.com/file/d/1ADdCb66VvndcBnRo38IIymL9HZsE4FNx/view?usp=sharing)- |
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[English](https://drive.google.com/file/d/1vyypcLpX6DTuogNxTeueRRl_PLvcbRsz/view?usp=sharing) |
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Test Data: |
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[Arabic](https://drive.google.com/file/d/1P1SnF4ZKOA1PBC6HRAYLxZa82eOGFR2F/view?usp=sharing)- |
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[English](https://drive.google.com/file/d/1HCPSAeJGNP5LtdIjAuu7ZbeFDTubMYDx/view?usp=sharing) |
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### Synthetic Data |
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About 200k synthetic images with segmentation maps. |
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[SynthData](https://drive.google.com/file/d/1PjfqY4ofK2jy85KbD1ITIma4JFz7fbuw/view?usp=sharing) |
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Code for Synthetic Data Generation: |
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https://github.com/HGamal11/Arabic_Synthetic_Data_Generator |
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### Citation |
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If you find this dataset useful for your research, please cite |
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``` |
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@article{hassan2021arabic, |
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title={Arabic Scene Text Recognition in the Deep Learning Era: Analysis on A Novel Dataset}, |
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author={Hassan, Heba and El-Mahdy, Ahmed and Hussein, Mohamed E}, |
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journal={IEEE Access}, |
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year={2021}, |
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publisher={IEEE} |
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} |
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``` |