Title:Automatic Text Segmentation and Recognition in Natural Scene Images Using Msocr
VOLUME: 15 ISSUE: 3
Author(s):S.R. Surem Samuel*, Christopher C. Seldev and S. Jinny Vinila
Affiliation:Department of ECE, CSI Institute of Technology, Thovalai, Department of CSE, St. Xavier’s Catholic College of Engineering, Chunkkankadai, Department of CSE, Noorul Islam Centre for Higher Education, Kumaracoil
Keywords:Text segmentation, connected component, segmentation algorithm, maximally stable, optical character recognition,
template matching.
Abstract:
Introduction: Segmentation and recognition of text from the scene image are a challenging
task due to blurred, low-resolution and small sized image.
Materials and Methods: Innovative methods have been proposed to address this problem and to
recognize the text from the 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. The Template matching algorithm is used
to extract the text character from the bounding box of the segmented image.
Results and Conclusion: Trained SVM classifier is used to classify the image containing text and
non-text region. Performances are analyzed based on the recall rate, precision, accuracy, and Fmeasure.
From the experimental results, the accuracy of the proposed classifier was obtained as
95%.
