Abstract: Objective: It is an open problem in image analysis based seal verification that how to segment a binary seal image out of a check image without distortions. An adaptive morphological segmentation algorithm based on top-hat transformation is proposed to accurately extract a binary Chinese square seal from a bank check. Method: A grayscale square seal is extracted from a color bank check according to the color information. Different Chinese characters have different stroke features and background evenness. To process each character in the square seal respectively

the seal is divided into four sub-squares. The background across each sub-square of the grayscale seal image is smoothed by top-hat transformation. The size of structuring element in top-hat transformation might have a great influence on the segmentation. An adaptive method is proposed to iteratively estimate the proper size of the structuring element according to the local foreground area. In each sub-square

the optimal sizes of the structuring elements respectively for the imprint frame and the character are estimated respectively. With their optimal structuring elements

the character and the imprint frame in each sub-square are filtered by top-hat transform and binarized by Otsu’s method. Result: The experiment result shows that when segmenting 350 different square seals in bank checks

2 segmented seals have distortions. Conclusion: The proposed algorithm can correctly segment Chinese characters with intricate and dense strokes in a bank check square seal. Adhesion and incompleteness distortions in the segmentation results are reduced

even when the original square seal has a poor quality.