ICDAR 2013 Dataset

Load ICDAR 2013 dataset in Python fast with one line of code. 462 images for near-horizontal text detection tasks. Stream ICDAR 2013 while training ML models.

Visualization of ICDAR 2013 dataset on the Activeloop Platform
ICDAR 2013 dataset
What is ICDAR 2013 Dataset?
The ICDAR 2013 dataset focuses on text content extraction from born-digital pictures, such as those used online and by email (born-digital images are media files created for online transmission). The ICDAR 2013 dataset comprises of 462 photos, including 229 for the training set and 233 for the test set. Text localization, text segmentation, and word recognition are all challenges relevant to the need to conduct text extraction from born-digital pictures. 
Download ICDAR 2013 Dataset in Python
Instead of downloading the ICDAR 2013 dataset in Python, you can effortlessly load it in Python via our open-source package Hub with just one line of code.
Load ICDAR 2013 Dataset Text Localization Training Subset in Python
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import hub
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ds = hub.load("hub://activeloop/icdar-2013-text-localize-train")
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Load ICDAR 2013 Dataset Text Localization Testing Subset in Python
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import hub
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ds = hub.load("hub://activeloop/icdar-2013-text-localize-test")
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Load ICDAR 2013 Dataset Text Segmentation Training Subset in Python
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import hub
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ds = hub.load("hub://activeloop/icdar-2013-segmentation-train")
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Load ICDAR 2013 Dataset Text Segmentation Testing Subset in Python
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import hub
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ds = hub.load("hub://activeloop/icdar-2013-segmentation-test")
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Load ICDAR 2013 Dataset Word Recognition Training Subset in Python
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import hub
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ds = hub.load("hub://activeloop/icdar-2013-word-recognition-train")
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Load ICDAR 2013 Dataset Word Recognition Testing Subset in Python
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import hub
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ds = hub.load("hub://activeloop/icdar-2013-word-recognition-test")
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ICDAR 2013 Dataset Structure
ICDAR 2013 Data Fields
For Text Localization
images : tensor containing images.
boxes/box: tensor coordinates of bounding boxes.
boxes/labels: tensor containing labels of bounding boxes.
For Text Segmentation
images : tensor containing images.
center_coordinates : tensor containing center coordinates of bounding box. For visibily on hub a 3rd value 2 is added additionally. Thus x, y, 2.
boxes/box: tensor containing coordinates of bounding boxes.
boxes/labels: tensor containing labels of bounding boxes.
masks/mask:  tensor containing binary mask.
masks/mask_pixel: tensor containing pixel values.
For Word Recognition
images : tensor containing images.
labels: tensor containing labels corresponding to the images.
ICDAR 2013 Data Splits
The ICDAR 2013 Text Localization training set is composed of 229 images and ground truth files.
The ICDAR 2013 Text Localization testing set​ is composed of 233 images and ground truth files.
The ICDAR 2013 Text Segmentation training set​ is composed of 229 images and ground truth files.
The ICDAR 2013 Text Segmentation testing set​ is composed of 233 images and ground truth files.
The ICDAR 2013 Word Recognition training set​ is composed of 848images and ground truth files.
The ICDAR 2013 Word Recognition testing set​ is composed of 1095 images and ground truth files.
How to use ICDAR 2013 Dataset with PyTorch and TensorFlow in Python
Train a model on ICDAR 2013 dataset with PyTorch in Python
Let's use Hub's built-in PyTorch one-line dataloader to connect the data to the compute:
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dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
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Train a model on ICDAR 2013 dataset with TensorFlow in Python
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dataloader = ds.tensorflow()
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Additional Information about ICDAR 2013 Dataset
ICDAR 2013 Dataset Description
Homepage: https://rrc.cvc.uab.es/?ch=2​
Paper: Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., i Bigorda, L. G., Mestre, S. R., ... & De Las Heras, L. P. (2013, August). ICDAR 2013 robust reading competition. In 2013 12th International Conference on Document Analysis and Recognition (pp. 1484-1493). IEEE.
ICDAR 2013 Dataset Curators
Dimosthenis Karatzas, Faisal Shafait, Seiichi Uchida, Masakazu Iwamura.
ICDAR  2013 Dataset Licensing Information
Hub users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license. 
If you're a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!
ICDAR 2013 Dataset Citation Information
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@inproceedings{karatzas2013icdar,
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  title={ICDAR 2013 robust reading competition},
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  author={Karatzas, Dimosthenis and Shafait, Faisal and Uchida, Seiichi and Iwamura, Masakazu and i Bigorda, Lluis Gomez and Mestre, Sergi Robles and Mas, Joan and Mota, David Fernandez and Almazan, Jon Almazan and De Las Heras, Lluis Pere},
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  booktitle={2013 12th International Conference on Document Analysis and Recognition},
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  pages={1484--1493},
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  year={2013},
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  organization={IEEE}
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}
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ICDAR 2013 Dataset FAQs
What is the ICDAR 2013 dataset for Python?
The ICDAR 2013 dataset focuses on textual content extraction from born-digital pictures, such as those used in website and email communications. The dataset. The dataset contains word-level annotations.
What is the ICDAR 2013 dataset used for?
The ICDAR 2013 dataset is a standard benchmark for evaluating near-horizontal text detection. It is also commonly used in the domain of machine learning for text localization, text segmentation, and word recognition.
 How to download the ICDAR 2013 dataset in Python?
You can load ICDAR 2013 dataset fast with one line of code using the open-source package Activeloop Hub in Python. See detailed instructions on how to load ICDAR 2013 Text Localization dataset training subset or ICDAR 2013 Text Localization dataset testing subset in Python.