Line Segmentation Approach for Ancient Palm Leaf Manuscripts Using Competitive Learning Algorithm

Line segmentation is very crucial in handwritten text recognition/analysis task. A new text line extraction scheme based on a data clustering algorithm is proposed. Our approach starts by determining the number of lines and setting up text line mid points' initial positions using a modified piece-wise projection profile technique. We apply afterwards competitive learning algorithm to adaptively move those mid points according to the geometrical information of connected components in the document page to form lines. Borders between text lines are defined so that they can be used to separate touching components that spread over multiple lines. The proposed method is robust in handling documents with skewed, fluctuated, or discontinued text lines. Experimental evaluations were made on a data set of Khmer ancient palm leaf manuscripts.