Unsupervised Evaluation Methods Based on Local Gray-Intensity Variances for Binarization of Historical Documents

We attempt to evaluate the efficacy of six unsupervised evaluation method to tune Sauvola´s threshold in optical character recognition (OCR) applications. We propose local implementations of well-known measures based on gray-intensity variances. Additionally, we derive four new measures from them using the unbiased variance estimator and gray-intensity logarithms. In our experiment, we selected the well binarized images, according each measure, and computed the accuracy of the recognized text of each. The results show that the weighted and uniform variance (using logarithms) are suitable measures for OCR applications.