Document Image Binarization: Evaluation Of Algorithms

The extraction of a binary image from a gray level image is a common image processing operation particularly for document image analysis and optical character recognition. Various methods for this task are described in the literature including global and adaptive binarization. This paper evaluates three adaptive binarization techniques viz., a contrast measure approach, a weighted running average approach and a second derivative approach, and compares them to global binarization methods. Experiments with noisy document (postal letter mail) images lead to the following conclusions. Image contrast binarization often yields nearly the same results as the edge operator, with considerably less computation and is less sensitive to parameter settings. In addition, the edge operator is more sensitive to image resolution than the contrast operator. The weighted running-average approach is highly sensitive to the parameters involved in the calculation of the average but produces a quick binarization.