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ICDAR 2013 Dataset

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Visualization of the  ICDAR 2013 dataset in the Deep Lake UI

ICDAR 2013 dataset

What is ICDAR 2013Dataset?

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 2013Dataset in Python

Instead of downloading the ICDAR 2013 dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load ICDAR 2013 Dataset Text Localization Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/icdar-2013-text-localize-train")

Load ICDAR 2013 Dataset Text Localization Testing Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/icdar-2013-text-localize-test")

Load ICDAR 2013 Dataset Text Segmentation Training Subset in Python 

				
					import deeplake
ds = deeplake.load("hub://activeloop/icdar-2013-segmentation-train")

Load ICDAR 2013 Dataset Text Segmentation Testing Subset in Python 

				
					import deeplake
ds = deeplake.load("hub://activeloop/icdar-2013-segmentation-test")

Load ICDAR 2013 Dataset Word Recognition Training Subset in Python 

				
					import deeplake
ds = deeplake.load("hub://activeloop/icdar-2013-word-recognition-train")

Load ICDAR 2013 Dataset Word Recognition Testing Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/icdar-2013-word-recognition-test")

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 visibility on Deep Lake, 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 Text Recognition
 
  • images: tensor containing images.
  • labels: tensor containing labels corresponding to the images.
ICDAR 2013 Data Splits

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 Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)
Train a model on ICDAR 2013 dataset with TensorFlow in Python
				
					dataloader = ds.tensorflow()

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

Deep Lake 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
				
					@inproceedings{karatzas2013icdar,
  title={ICDAR 2013 robust reading competition},
  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},
  booktitle={2013 12th International Conference on Document Analysis and Recognition},
  pages={1484--1493},
  year={2013},
  organization={IEEE}
}

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 a 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 can I use ICDAR 2013 dataset in PyTorch or TensorFlow?

You can load ICDAR 2013 dataset fast with one line of code using the open-source package Activeloop Deep Lake 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.

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