This is the code used to evaluate the convolutional preprocessor's performance for 10,000 images in the BRNO dataset. See the article on coding.vision or the more in-depth version as published in the journal.
It uses the set of trained convolution filters avaiable in conv-filters/cov_filters_-666.dat. Images from the dataset are not included since they'd take too much space; they should be placed in the data/brno/ directory and have the names mentioned in validation.txt.
The plots/ directory contains the plots referenced in the article (generated when running the script).
The code might be a little bit messy as I've never thought I'd actually publish it.
python3 tester.py
Expected output:
dan@lasher:~/work/convolutional-preprocessor$ python3 tester.py
Total chars: 569597
Avg. chars: 56.95400459954005
Avg. processed CER: 0.38477649218011123
Avg. processed WER: 0.5933691789730595
Avg. processed LCSER: 24.987501249875013
Processed CER stdev: 0.35907653087055835
Processed WER stdev: 0.4815551725494633
Processed LCSER stdev: 24.543682359532433
Avg. NON-processed CER: 0.8669550578680915
Avg. NON-processed WER: 0.9032174848878798
Avg. NON-processed LCSER: 48.83411658834117
NON-Processed CER stdev: 0.31442282213706674
NON-Processed WER stdev: 0.29750312162243775
NON-Processed LCSER stdev: 26.669697784976975
Avg. processed precision 0.7255293562720874
Avg. processed recall 0.73405452345541
Avg. NON-processed precision 0.15593248371006405
Avg. NON-processed recall 0.17280730123032162
Scores of proc vs non-proc: 3848.1496982932567 8670.417533738775
Improvement percent: 55.61748112684148