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Automatic Text Segmentation and Recognition in Natural Scene Images Using Msocr

Author(s):

Surem Samuel SR*, Seldev Christopher C and VinilaJinny S   Pages 1 - 12 ( 12 )

Abstract:


Segmentation and recognition of text from the scene image is a challenging task due to blurred, low-resolution and small sized image. Innovative methods have been proposed to address this problem and to recognize the text from natural scene image. The acquired image is pre-processed by the YUV channel conversion technique and the Y channel image is converted to a gray scale image. Connected Component Based Text Segmentation Algorithm (CCBTSA) and MSER methods are used for segmentation and recognition of text using Optical Character Recognition (OCR). GLCM and FOS features are extracted from the segmented region. Template matching algorithm is used to extract the text character from the bounding box of segmented image. Trained SVM classifier is used to classify the image containing text and non-text region.Performances are analysed based on the recall rate, precision, accuracy and F-measure. From the experimental results, the accuracy of the classifier was obtained as 95%.

Keywords:

Text segmentation, Connected Component Based Text Segmentation Algorithm, Maximally Stable Optical Character Recognition, Template matching

Affiliation:

Assistant professor, Department of ECE, CSI Institute of Technology, Thovalai, Professor, Department Of CSE, St.Xavier s Catholic College of Engineering, Chunkkankadai, Associate Professor, Department of CSE, Noorul Islam Centre for Higher Education, Kumaracoil



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