Image Text Extraction and Recognition using Hybrid Approach of Region Based and Connected Component Methods

Image mining concerns the extraction of implicit knowledge, image data relationship or other patterns not explicitly stored in the images. Text in images is one of the powerful sources of high-level semantics. If these text occurrences could be detected, segmented, and recognized automatically, they would be a valuable source of high-level semantics for indexing and retrieval. The proposal in this work entitled as ―Image Text Extraction and Recognition Using Hybrid Approach of Region Based and Connected Component Methods‖ has been developed to detect, extract and recognize the text regions and the system is based on efficient edge detectors, connected component methods and optical character recognition. Text detection and extraction in images is important for content based image analysis. This problem is challenging due to the complex background, the non-uniform illumination, and the variations of text font, size and line orientation. The proposed method in this work develops an efficient text extraction and recognition methods that utilizes the concept of morphological operations using MATLAB. Existing text extraction methods – edge based and connected components produce better results when applied separately. But these methods produce more false positives. So it is proposed to take advantage of both methods and combine these methods in the proposed system. The result shows that the proposed methodology yields better results than the other two methods. Keywords—Text region detection, Clustering, Binarization, Segmentation, Recognition.