Automatic Text Segmentation and Recognition in Natural Scene Images Using Msocr - Samuel, S.R. S.; Seldev, Christopher C.; Vinila, S. Jinny (Current Signal Transduction Therapy, 2020)
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%.
Keywords: maximally stable