CHARACTER ANALYSIS USING SPACE IN HANDWRITING IMAGE TO DETERMINE SPECIALIZATION IN BUSINESS
Abstract
Specialization selection in business has been a subject of research study for several years. In our work, it begins with collecting the handwriting samples on plain white A4 size paper. 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. Skew-normalization techniques have been applied to find out space between lines, words and letters in handwriting images after segmentation of lines, words and characters have been performed. 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. The characters are then matched with the requirements of each specialization. Accordingly, the candidates select their specialization and their performance in the exam has proved it to be a healthy match for them. This paper focuses on selection of specialization in business based on behavior from space analysis in handwritten document. The proposed method was tested on more than 500 text images 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 after skew normalization of a document. The experimental result shows that proposed algorithm achieves more than 63% accuracy for all type skew angles.
Keywords:
Skew Normalization, Space Analysis, Character Analysis, Business Requirements, Performance AnalysisDownloads
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