Text Extraction and Recognition from the Normal Images using MSER Feature Extraction and Text Segmentation Methods

Abstract

Image mining is concerned with the extraction of contained information, image information connection or other patterns not clearly stored in the images. Text in images is one of the dominant features and its extraction is a big task. If this type of text could be segmented, detected, extracted and recognized automatically, than it would be a precious source of high-level retrieval process. In the research work, text extraction and recognition from the normal images using MSER feature extraction and text segmentation methods has been developed to detect the text regions and the system is based on efficient optical character recognition process. Text extraction and recognition from the normal images is important for content based image analysis. This problem is challenging due to the complex background of images, reflection of light in images and shadow portion presented in images. The proposed technique in this work develops a well-organized text extraction and recognition methods that utilizes the concept of morphological operations using digital image processing. Existing text extraction method, namely, region based method produces enhanced results when applied on the normal images. The advantage of segmentation for the feature extraction of text region is proposed in the system.