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TomStog / curved-text-alignment

A function that takes as input a cropped text line image, and outputs the dewarped image.
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bresenham-algorithm curved-text dewarp dewarping generalized-additive-models greek-language greek-texts image-processing python rectification text-alignment text-rectification

readme

Text Line Dewarping

Dependencies

pip3 install -r requirements.txt

NOTE: If you are using pyenv to install older versions of Python, you might need to install development versions of libsqlite3x, ncurses, readline, and tkinter. For example, on Fedora: dnf install libsq3-devel ncurses-devel readline-devel tk-devel.

Running

To process the entire image

Run the dewarp.py script :

python ./dewarp.py ./sample.png ./output.png

To process the image only where there's text

Run the tight_dewarp.py script :

python ./tight_dewarp.py ./sample.png ./output.png

Both functions exhibit comparable performance, with no discernible advantage in either. The primary distinction lies in their operational scope: dewarp.py operates across the entire image, whereas tight_dewarp.py specifically tracks the leftmost and rightmost black pixels within Otsu's threshold image, concentrating its efforts within that identified range.

Steps

1) Load Image :

Original image

2) Convert from RGB to Grayscale :

Output image

3) Apply Otsu's Thresholding Method, Erosion and then Dilation :

Original image

4) Calculate curve using Generalized Additive Model :

Output image

5) Final Image :

Output image

Greek Text Example

1) Input Image :

Output image

2) Output Image :

Output image

Rectification

1) Input Image :

Output image

2) Semi-processed Image :

Output image

3) Output Image :

Output image

The rectification dataset can be viewed and downloaded through this link.

Results and Performance

The number of splines used for the initial curve estimation is 8, and the number of splines used for the final alignment is 12.

Warping Function DW Word Error Rate w/o Rectification Character Error Rate w/o Rectification Word Error Rate w/ Rectification Character Error Rate w/ Rectification
y = -x 99.86% 0.9440 0.5063 0.1552 0.0237
y = x2 99.86% 1.3352 0.8339 0.3973 0.0620
y = -x3 99.88% 1.1067 0.6613 0.1838 0.0318
y = x4 99.92% 1.7962 0.7910 0.3772 0.0575

Suppose we aim to improve performance for the y = x2 scenario by identifying an optimal set of numbers. Below is the variation in CER and WER scores based on the number of splines used:

cer

wer

Citation

If you have found value in this repository, we kindly request that you consider citing it as a source of reference:

Stogiannopoulos, Thomas. “Curved Line Text Alignment: A Function That Takes as Input a Cropped Text Line Image, and Outputs the Dewarped Image.” GitHub, December 1, 2022. https://github.com/TomStog/curved-text-alignment.

For more information, you can also check my paper "Curved Text Line Rectification via Bresenham’s Algorithm and Generalized Additive Models" here.

@article{Stogiannopoulos2024CurvedTL,
  title={Curved Text Line Rectification via Bresenham’s Algorithm and Generalized Additive Models},
  author={Thomas Stogiannopoulos and Ilias Theodorakopoulos},
  journal={Signals},
  year={2024},
  url={https://api.semanticscholar.org/CorpusID:273595704}
}