An Improved Method for Text Segmentation and Skew Normalization of Handwriting Image

This paper proposed an off-line cursive handwriting segmentation method and an efficient skew normalization process of the handwritten document. The proposed segmentation method based on horizontal and vertical projection, which has been already used for different purposes in handwriting analysis. But to tolerate the text lines overlapping and multi-skewed text lines, present work implements modified version of horizontal and vertical projection, which can segment the text lines and words even if text lines are overlapped. Present work also proposed a skew normalization method which is based on orthogonal projection toward the x-axis. The proposed method was tested on more than 550 text images of IAM database and sample handwriting image which are written by the different writer on the different background. The experimental result shows that proposed algorithm achieves more than 96% accuracy.

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