Detection of Text Using Connected Component Clustering and Nontext Filtering - Elakkiya, S; Kavitha, T (International Journal of Computer Sciences and Engineering, 2015)
Several methods have been developed for text detection and extraction to achieve accuracy for natural scene text and for multi-oriented text. However most of the methods use classifier to improve text detection accuracy. So this paper uses two machine learning classifiers one is to generate candidate region and the other filters nontext. Here connected components (CCs) in images are extracted by using the maximally stable extremal region algorithm. These extracted CCs are partitioned into clusters so that we can generate candidate regions. An AdaBoost classifier is trained to determine the adjacency relationship and cluster CCs by using their pair-wise relations. Since the scale, skew, and color of each candidate can be estimated from CCs, we can develop a text/nontext classifier for normalized images. This classifier will be based on multilayer perceptrons and we can control recall and precision rates with a single free parameter. Finally, the approach can be extended to exploit multichannel information and this method yields the state-of-the-art performance both in speed and accuracy.