OCR Accuracy Prediction Method Based on Blur Estimation

Abstract

In this paper, we propose an OCR accuracy prediction method based on a local blur estimation since blur is one of the important factors that mostly damage OCR accuracy. First, we apply the blur estimation on synthetic blurred images by using Gaussian and motion blur in order to investigate the relation between blur effect and character size regarding OCR accuracy. This relation is considered as a blur-character size feature to define a classifier. Finally, the classifier can separate characters of a given document into three classes: readable, intermediate, and non-readable classes. Therefore, the quality score of the document is inferred from the three classes. The proposed method is evaluated on a published database and on an industrial one. The correlation with OCR accuracy is also given to compare with the state-of-the-art methods.