IIIT5K-Words

The IIIT5K Words Dataset is a comprehensive collection of labeled word images, curated by the International Institute of Information Technology, Hyderabad (IIIT-H). It is designed to facilitate research and development in optical character recognition (OCR), word recognition, and related fields.

The dataset contains a diverse set of 5,000 word images, covering various fonts, styles, and sizes. Each word image represents a single English word and is accompanied by its corresponding ground truth label, providing accurate transcription for training and evaluation purposes.

Please refer: IIIT5K-Words official site

Note: In order to view mat files use this code

install requirements

!pip install shutil pymatreader

unzip the zip file

import shutil

shutil.unpack_archive('IIIT5K-Word_V3.0.tar.gz', 'data')

view mat files

from pymatreader import read_mat

testdata_mat = read_mat('testdata.mat')

testCharBound_mat = read_mat('testCharBound.mat')

testdata_mat

Key Features: - Size: The dataset comprises 5,000 word images, making it suitable for training and evaluating OCR algorithms. - Diversity: The dataset encompasses a wide range of fonts, styles, and sizes to ensure the inclusion of various challenges encountered in real-world scenarios. - Ground Truth Labels: Each word image is paired with its ground truth label, enabling supervised learning approaches and facilitating evaluation metrics calculation. - Quality Annotation: The dataset has been carefully curated by experts at IIIT-H, ensuring high-quality annotations and accurate transcription of the word images. - Research Applications: The dataset serves as a valuable resource for OCR, word recognition, text detection, and related research areas.

Potential Use Cases: - Optical Character Recognition (OCR) Systems: The dataset can be employed to train and benchmark OCR models, improving their accuracy and robustness. - Word Recognition Algorithms: Researchers can utilize the dataset to develop and evaluate word recognition algorithms, including deep learning-based approaches. - Text Detection: The dataset can aid in the development and evaluation of algorithms for text detection in natural scenes. - Font and Style Analysis: Researchers can leverage the dataset to study font and style variations, character segmentation, and other related analyses.

Citation: >@InProceedings{MishraBMVC12, author = "Mishra, A. and Alahari, K. and Jawahar, C.~V.", title = "Scene Text Recognition using Higher Order Language Priors", booktitle = "BMVC", year = "2012", }