A Comprehensive Study On Handwritten Character Recognition System (original) (raw)
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Improving Various Offline Techniques used for Handwritten Character Recognition : A Review
International Journal of Computer Applications, 2012
Handwritten character recognition is always an advanced area of research in the field of image processing and pattern recognition and there is a large demand for OCR on offline hand written documents. Even though, sufficient studies have performed from history to this era, paper describes the techniques for converting textual content from a paper document into machine readable form. The computer actually recognizes the characters in the document through a revolutionizing technique called Optical Character Recognition (OCR). There are many paper deals with issues such as hand-printed character and cursive handwritten word recognition which describes recent achievements, difficulties, successes and challenges in all aspects of handwriting recognition. Their many papers present a new approach which improves current handwriting recognition systems. Some experimental results are included. Selection of a relevant feature extraction method is probably the single most important factor in achieving high recognition performance with much better accuracy in character recognition systemsn this paper, we describe the formatting guidelines for IJCA Journal Submission.
Character Recognition of Offline Handwritten English Scripts: A Review
Character recognition is a process by which computer recognizes letters, numbers or symbols and turn them into digital form that a computer can use. In today's environment character recognition has gained lot of concentration in the field of pattern recognition. Handwritten character recognition is useful in cheque processing in banks,form processing systems and many more. Character recognition is one of the well-liked and challenging area of research. In future character recognition create paperless environment. In this paper we describe the detail study on existing method for handwritten character recognition. We provide a literature review on various techniques used in offline english character recognition.
A Technique for Offline Handwritten Character Recognition
Offline handwritten character recognition has been one of the most engrossing and challenging research areas in the field of pattern recognition in the recent years. Offline handwritten character recognition is a very problematic research area because writing styles may vary from one user to another. In this paper a proposed technique for offline handwritten Gurmukhi character recognition has been presented. The success rate of our proposed scheme depends upon the feature extraction technique which has been applied in this work. We have proposed a feature extraction technique named as Neighborhood Foreground Pixels Density technique. As there could be some insignificant feature values so to reject those we have used a dimensionality reduction technique namely Principal component analysis (PCA). Maximum recognition accuracy of 91.95% has been achieved with SVM (Radial Basis function kernel) classifier by using 10 fold cross validation test method.
Offline Handwritten Character Recognition Techniques using Neural Network : A Review
2012
This paper presents detailed review in the field of Off-line Handwritten Character Recognition. Various methods are analyzed that have been proposed to realize the core of character recognition in an optical character recognition system. The recognition of handwriting can, however, still is considered an open research problem due to its substantial variation in appearance. Even though, sufficient studies have performed from history to this era, paper describes the techniques for converting textual content from a paper document into machine readable form. Offline handwritten character recognition is a process where the computer understands automatically the image of handwritten script. This material serves as a guide and update for readers working in the Character Recognition area. Selection of a relevant feature extraction method is probably the single most important factor in achieving high recognition performance with much better accuracy in character recognition systems.
Study of Different Off-line Handwritten Character Recognition Algorithms for Various Indian Scripts
2013
Handwritten recognition is an area of research where many researchers have presented their work and is still an area under research to achieve higher accuracy. In past collecting, storing and transmitting information in form of handwritten script was the most convenient way and is still prevailing as a convenient medium in the era of digital technology. As technology has advanced tablet and many similar devices allows humans to input data in form of handwriting. Use of paper to write handwritten text, converting to an image using scanner, identifying handwritten characters from the image is known as off-line handwritten text recognition is a challenging area due to the fact that different people will have different style of writing and all scripts have their own character set and complexities to write text. Many researchers have presented their work and many algorithms are proposed to recognize handwritten and printed characters. One can trace extensive work for off-line handwritten recognition for English and Arabic script. This paper presents review of work to recognize off-line handwritten text for various Indian language scripts. Paper reviews methodologies with respect to the phases of character recognition.
Offline English Hand Written Character Recognition Using Neural Network
2013
Image processing and pattern recognition plays a lead role in handwritten character recognition. The recognition of handwriting can,however, still be considered an open research problem due to its substantial variation in appearance .There are four main steps of handwritten character recognition-Data collection and pre -processing, segmentation feature extraction and classification. The main objective of this research is to find a new solution for handwritten text recognition of different fonts and styles by improving the design structure of the feature extraction. The main aim of this paper is to propose a fast and easy to use feature extraction method that obtains a good performance. This study focuses on isolated characters. Diagonal feature extraction scheme for recognizing off-line handwritten characters is proposed in this work in addition efficient feature such as Eigen value an d mean value are also used which improves accuracy to recognize character. Diagonal features are p...
Analytical Review of Preprocessing Techniques for Offline Handwritten Character Recognition
Preprocessing techniques are the first step in a character recognition system. This paper deals with the various preprocessing techniques involved in character recognition system with different kind of images ranges from simple handwritten form based documents and documents containing colored and complex background and varied intensities. Here, we are going to discuss all important preprocessing techniques like skew detection and correction, image enhancement techniques of contrast stretching, binarization, noise removal techniques, normalization and segmentation, morphological processing techniques.
Recognition of handwritten characters has become a difficult problem because of the high variability and ambiguity in the character shapes written by individuals. Some of the problems encountered by researchers include selection of efficient feature extraction method, long network training time, long recognition time and low recognition accuracy. However, many feature extraction techniques have been proposed in literature to improve overall recognition rate although most of the techniques used only one property of the handwritten character. This research focuses on developing a feature extraction technique that combined three characteristics (stroke information, contour pixels and zoning) of the handwritten character to create a global feature vector. A hybrid feature extraction algorithm was developed to alleviate the problem of poor feature extraction algorithm of online character recognition system. Besides, this research work also focused on alleviating the problem of standard backpropagation algorithm based on 'error adjustment'. A hybrid of modified Counterpropagation and modified optical backpropagation neural network model was developed to enhance the performance of the proposed character recognition system. Experiments were 90 performed with 6200 handwriting character samples (English uppercase, lowercase and digits) collected from 50 subjects using G-Pen 450 digitizer and the system was tested with 100 character samples written by people who did not participate in the initial data acquisition process. The performance of the system was evaluated based on different learning rates, different image sizes and different database sizes. The developed system achieved better performance with no recognition failure, 99% recognition rate and an average recognition time of 2 milliseconds.
Preprocessing techniques are the first step in a character recognition system. This paper deals with the various preprocessing techniques involved in character recognition system with different kind of images ranges from simple handwritten form based documents and documents containing colored and complex background and varied intensities. Here, we are going to discuss all important preprocessing techniques like skew detection and correction, image enhancement techniques of contrast stretching, binarization, noise removal techniques, normalization and segmentation, morphological processing techniques.