An efficient method for character analysis using space in handwriting image

Handwriting analysis has been a subject of research study for several decades. It is a multi stage procedure. In our work, it begins with collecting the handwriting samples on plain white A4 size paper. Preprocessing steps such as binarization and noise removal etc are performed for better recognition. Initially color image or gray scale image is taken as an input then thresholding is done to convert the image into binary image and noise removal technique is also applied. Then line segmentation, word segmentation and character segmentation have been performed. After each segmentation process, normalization techniques have been applied for normalization purpose to find out space between lines, words and letters in handwriting images. Finally, the mean of the space between all the closed loops formed by the characters has been found out and compared with the word spaces to determine the character. This paper focuses on determination of behavior based on space analysis in handwritten document. The proposed method was tested on more than 500 text image of IAM database and sample handwriting images which are written by different writers on different backgrounds, detects the exact space in between lines, words and characters before and after skew normalization of a document. The experimental result shows that proposed algorithm achieves more than 63% accuracy for all type skew angles.