License plate recognition with multi-threshold based on entropy

Among all the existing segmentation techniques, thresholding technique is one of the most popular one due to its simplicity, robustness and accuracy. Multi-thresholding is an important operation in many analyses which is used in many applications. Selecting correct thresholds to get better result is a critical issue. In this research, a multilevel thresholding method is proposed based on combination of maximum entropy. The maximum entropy thresholding algorithm selects several threshold values by maximizing the cross entropy between the original image and the segmented image. This method can effectively integrate partial range of the image histogram. The proposed algorithm is compared with single thresholding method based on maximum entropy and multilevel thresholding method The proposed multi thresholding method is tested on license plate application. From the experiment, multi-threshold method further improved to increase the segmentation accuracy in the future.