A Robust Multilingual Text Detection Approach Based on Transforms and Wavelet Entropy

In the present work, we describe the study and performance of a robust text detection method in color and regular images. The system employs two important stages: Firstly the combination of wavelet transforms and gabor filter is applied to extract sharpened edges and textural features of a given input image. In the second stage, wavelet entropy is imposed to get an energy value of a resulted gabor image in order to find the high frequency texture elements of a processed one to determine the true text region of an image. Experiments are conducted on standard ICDAR database and with multi-lingual dataset. Experimental results show that the proposed method is able to detect text of different font size, complex background and contrast. Furthermore, the system performance surpasses the existing method in terms of detection accuracy.