Connected Component Clustering Based Text Detection with Structure Based Partition and Grouping

Abstract

Extraction of text from natural scene images is a challenging problem because of its complex backgrounds and large variations of text patterns. In this paper, we presents an innovative scene text detection algorithm with the help of two machine learning classifiers: one for candidate generation and the other which filters out non text ones. And the enhancement technique followed by a text string detection with arbitrary orientations based on structure- based partition and grouping. To extracted the connected components (CCs) in images an algorithm us used ,popularly known as maximally stable extremal region algorithm. These extracted CCs are partitioned into clusters and generate candidate regions. However, the methods use AdaBoost classifier to training the samples and improve the text detection accuracy. The scale, skew, and color of each candidate can be estimated from CCs, and filtered the non text from the normalized images. To find the text string consists of two steps: A) Image partition to detect the text character candidates by using gradient magnitude of character components and B)Text character grouping to detect text strings by using structural analysis of text characters and then merges them into text string for example size of character,distance between neighboring characters.To improve efficiency and accuracy, our algorithms are carried out in multi-scales. The proposed system yield very high identification accuracy and take less time for detection as compared to the existing system.