A test implementation of the Stroke Width Transform algorithm described in the paper Detecting Text in Natural Scenes with Stroke Width Transform (PDF here):
We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows. Extensive testing shows that the suggested scheme outperforms the latest published algorithms. Its simplicity allows the algorithm to detect texts in many fonts and languages.
To run SWT with connected components against the text.jpg example image, execute
python main.py images/text.jpgGiven the following image ...
... it will find these connected components:
A conda environment is available in environment.yaml. To create and activate it, run
conda env create -f environment.yaml
conda activate swt@InProceedings{epshtein2010detecting,
author = {Epshtein, Boris and Ofek, Eyal and Wexler, Yonatan},
title = {Detecting Text in Natural Scenes with Stroke Width Transform},
year = {2010},
month = {June},
abstract = {We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows. Extensive testing shows that the suggested scheme outperforms the latest published algorithms. Its simplicity allows the algorithm to detect texts in many fonts and languages.},
publisher = {IEEE - Institute of Electrical and Electronics Engineers},
url = {https://www.microsoft.com/en-us/research/publication/detecting-text-in-natural-scenes-with-stroke-width-transform/},
}The code in this repository is made available under the MIT license (see LICENSE.md).