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