Character Analysis Report system
Abstract : Optical Character Recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into its constituent characters. Despite decades of intense research, developing OCR with capabilities comparable to that of human still remains an open challenge. Due to this challenging nature, researchers from industry and academic circles have directed their attentions towards Optical Character Recognition. Over the last few years, the number of academic laboratories and companies involved in research on Character Recognition has increased dramatically. This research aims at summarizing the research so far done in the field of OCR. It provides an overview of different aspects of OCR and discusses corresponding proposals aimed at resolving issues of OCR.
? The main reason is that the existing methods focus on particular applications and specific character shapes for person behavior identification.
? On the other hand, in contrast to the existing methods, the proposed system does not target any particular application and is not limited to specific characters.
? The way the proposed method extracts features and defines rules based on graphologist is different from the above-mentioned existing methods.
? Unlike existing methods that use characters, words and sentences for behavioral analysis, which often require trained individuals and are pertaining to specific applications, we propose an automatic method by analyzing a few handwritten English lowercase characters from a to z to identify person behaviors.
? OCR is a complex problem because of the variety of languages, fonts and styles in which text can be written, and the complex rules of languages etc.
? This paper introduces the reader to the problem. It enlightens the reader with the historical perspectives, applications, challenges and techniques of OCR.
? Character recognition is not a new problem but its roots can be traced back to systems before the inventions of computers.
? The earliest OCR systems were not computers but mechanical devices that were able to recognize characters, but very slow speed and low accuracy.
• Several methods have been proposed for predicting person behaviors using graphology based handwriting in literature; however, such methods expect human intervention to identify behaviors.
• Though the above two methods are related to person behavior identification, the scopes and the ways they extract features are different from the proposed method.
• However, the scope of the proposed work is to study the attributes of characters to identify different types of personal behaviors.
• The key advantage of the proposed system is that it is independent of application, character, age, gender, ink, paper, pen, etc.
• We can argue that the proposed system tends to generalization compared to the existing methods.
? The segmented characters are then processes to extract different features.
? Based on these features, the characters are recognized. Different types of features that can be used extracted from images are moments etc.
? The extracted features should be efficiently computable, minimize intra-class variations and maximizes inter-class variations.
? To measure the performance of the proposed method, we use accuracy, which is defiend as the total number of correct responses given by user (agree or disagree for the proposed system prediction) divided by the total number of characters.
? When a person writes touching characters, the performance of the proposed system degrades because it accepts individual characters as the input for person behavior identification in this work.
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