Evaluation:
The performance evaluation will be based mainly on DIBCO-2009 evaluation measures. In the following we provide an example of a well established technique that counts True Positive (TP), False Positive (FP) and False Negative (FN) pixels in order to calculate Recall and Precision metrics.
- A pixel is classified as True Positive (TP) if it is ON in both the GT and the Binarization Result images.
- A pixel is classified as False Positive (FP) if it is ON only in the Binarization Result image.
- A pixel is classified as False Negative (FN) if it is ON only in the GT image.
| If CTP the number of TP pixels, CFP the number of FP pixels and CFN the number of FN pixels, Recall (RC) and Precision (PR) metrics are given as follows:
RC = CTP/(CFN+CTP)
PR = CTP/(CFP+CTP)
The overall metric that will be used for ranking is the F-Measure (FM) which is calculated as follows:
FM = (2xRCxPR / (RC+PR)) x 100 %
| | Example: | 
| | CTP = 27, CFP = 11, CFN = 8, RC = .77, PR = .71, FM = 73.9% |
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