Two-Stage Rejection Algorithm to Reduce Search Space for Character Recognition in Ocr
Two-Stage Rejection Algorithm to Reduce Search Space for Character Recognition in OCR
Srivardhini Mandipati, Gottumukkala Asisha, Preethi Raj S, and Chitrakala S
Department of Computer Science and Engineering, Easwari Engineering College, Chennai, India
Abstract. Optical Character Recognition converts text in images into a form that the computer can manipulate. The need for faster OCRs stems from the abundance of such text. This paper presents a Two-Stage Rejection Algorithm for reducing the search space of an OCR. It is tacit that the reduction in search space expedites an OCR. Preprocessing operations are applied on the input and features are extracted from them. These feature vectors are clustered and the Two-Stage Rejection Algorithm is applied for character recognition. With about the same character recognition rate as other OCRs, an OCR reinforced with the Two-Stage Rejection Algorithm is considerably faster.
Keywords: Optical Character Recognition, Feature Extraction, K-means.
1 Introduction Optical character recognition has been an active area of research for many decades. The fact that OCRs have the potential to simplify data entry in the future adds value to research in this area. OCRs use various pattern matching techniques for character recognition. Most OCRs typically use classifiers like SVM or neural networks for character recognition. The training process for these classifiers is time consuming. Moreover, with an increase in the number of classes, the comparisons made increases and consequently the time taken for character recognition increases. Hence, they cannot be easily extended to recognize characters from additional languages. The proposed system uses a structural approach as opposed to statistical approach for feature extraction. The strength of the structural method over the statistical one is its representation of a pattern that is similar to the way human perceive it. The structural features help
Srivardhini Mandipati, Gottumukkala Asisha, Preethi Raj S, and Chitrakala S
Department of Computer Science and Engineering, Easwari Engineering College, Chennai, India
Abstract. Optical Character Recognition converts text in images into a form that the computer can manipulate. The need for faster OCRs stems from the abundance of such text. This paper presents a Two-Stage Rejection Algorithm for reducing the search space of an OCR. It is tacit that the reduction in search space expedites an OCR. Preprocessing operations are applied on the input and features are extracted from them. These feature vectors are clustered and the Two-Stage Rejection Algorithm is applied for character recognition. With about the same character recognition rate as other OCRs, an OCR reinforced with the Two-Stage Rejection Algorithm is considerably faster.
Keywords: Optical Character Recognition, Feature Extraction, K-means.
1 Introduction Optical character recognition has been an active area of research for many decades. The fact that OCRs have the potential to simplify data entry in the future adds value to research in this area. OCRs use various pattern matching techniques for character recognition. Most OCRs typically use classifiers like SVM or neural networks for character recognition. The training process for these classifiers is time consuming. Moreover, with an increase in the number of classes, the comparisons made increases and consequently the time taken for character recognition increases. Hence, they cannot be easily extended to recognize characters from additional languages. The proposed system uses a structural approach as opposed to statistical approach for feature extraction. The strength of the structural method over the statistical one is its representation of a pattern that is similar to the way human perceive it. The structural features help
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