This project aims to detect text regions in images using only image processing techniques with MSER (Maximally Stable Extremal Regions) and SWT (Stroke Width Transform). Utilized papers are

B. Epshtein, E. Ofek, and Y. Wexler. Detecting text in natural scenes with stroke width transform. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2963–2970, June 2010.

Á. González, L. M. Bergasa, J. J. Yebes, and S. Bronte. Text location in complex images. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pages 617–620, Nov 2012.

Y. Li and H. Lu. Scene text detection via stroke width. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pages 681–684, Nov 2012.

And also Tesseract-OCR tool is used optionally, as assistance to the algorithm.

Insall requirements.txt file

pip install -r requirements.txt

Or you can create conda environment with

conda create -f requirements.yml

For OCR assistance, install Tesseract from package manager

sudo apt install tesseract-ocr

Basic usage is

python text_detect.py -i <input-image>

You can give output path

python text_detect.py -i images/scenetext01.jpg -o <output-image>

More options available

python text_detect.py -i images/scenetext01.jpg -o <output-file> -d <light,dark,both,both+> -t

Option -i is image path, -o is output path, -d is SWT direction (default is both+), -t option chooses if Tesseract will be used. Normally Tesseract runs poorly if whole image given as input. But I use it for final decision of bounding boxes and it is not required all the time.

If you want to give whole image to Tesseract to see the impact of the algorithm, try this.

python text_detection.py -i images/scenetext01.jpg -f

For more detail (seeing intermediate steps), the usage given below is also available.

python text_detection_detail.py -i images/scenetext01.jpg -d both+ -t