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Handwritten Character Recognition of a Vernacular Language: The Odia Script - ethesis
Mohapatra, Ramesh Kumar Handwritten Character Recognition of a Vernacular Language: The Odia Script. PhD thesis. Optical Character Recognition, i. As of late, OCR technology has been utilized in most of the industries for better management of phd thesis character recognition documents.
OCR helps to edit the text, phd thesis character recognition, allow us to search for a word or phrase, and store it more compactly in the computer memory for future use and moreover, it can be processed by other applications. In India, phd thesis character recognition couple of organizations have designed OCR for some mainstream Indic dialects, for example, Devanagari, Hindi, Bangla and to some extent Telugu, Tamil, Gurmukhi, Odia, phd thesis character recognition, etc.
However, it has been observed that the progress for Odia script recognition is quite less when contrasted with different dialects. Any recognition process works on some nearby standard databases. Till now, no such standard database available in the literature for Odia script. Apart from the existing standard databases for other Indic languages, in phd thesis character recognition thesis, we have designed databases on handwritten Odia Digit, phd thesis character recognition, and character for the simulation of the proposed schemes.
In this thesis, four schemes have been suggested, one for the recognition of Odia digit and phd thesis character recognition three for atomic Odia character. Various issues of handwritten character recognition have been examined including feature extraction, the grouping of samples based on some characteristics, and designing classifiers. Also, different features such as statistical as well as structural of a character have been studied.
It is not necessary that the character written by a person next time would always be of same shape and stroke. Hence, variability in the personal writing of different individual makes the character recognition quite challenging. Standard classifiers have been utilized for the recognition of Odia character set. An array of Gabor filters has been employed for recognition of Odia digits. In this regard, each image is divided into four blocks of equal size. Gabor filters with various scales and orientations have been applied to these sub-images keeping other filter parameters constant.
The average energy is computed for each transformed image to obtain a feature vector for each digit. Further, a Back Propagation Neural Network BPNN has been employed to classify the samples taking the feature vector as input.
In addition, the proposed scheme has also been tested on standard digit databases like MNIST and USPS. Toward the end of this part, an application has been intended to evaluate simple arithmetic equation.
viii A multi-resolution scheme has been suggested to extract features from Odia atomic character and recognize them using the back propagation neural network. It has been observed that few Odia characters have a vertical line present toward the end. It helps in dividing the whole dataset into two subgroups, in particular, Group I and Group II such that all characters in Group I have a vertical line and rest are in Group II.
The two class classification problem has been tackled by a single layer perceptron. Besides, the two-dimensional Discrete Orthogonal S-Transform DOST coefficients are extracted from images of each group, subsequently, Principal Component Analysis PCA has been applied to find significant features. For each group, a separate BPNN classifier is utilized to recognize the character set.
Repository Staff Only: item control page. Handwritten Character Recognition of a Vernacular Language: The Odia Script. Abstract Optical Character Recognition, i. NIT Rourkela. Powered by. Login Create Account. Handwritten Character Recognition of a Vernacular Language: The Odia Script Mohapatra, Ramesh Kumar Handwritten Character Recognition of a Vernacular Language: The Odia Script.
PDF 7Mb. Odia Script; Database; Feature Extraction; Chain Code Histogram; Support Vector Machine; Back Propagation Neural Network; Deep Belief Network. Sanat Kumar Behera. Majhi, B and Jena, S K.
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