Scene text detection images with pyramid image and MSER enhanced

Text detection from images in natural scene is one of the most active research areas. It still remains a challenge for researchers because of the complexity of the image in the wild specifically their background. The state of the text presents also different problems of localization such as size, font, color and orientation. This paper presents a new method based on the location of the concentration areas of text candidates in first step. This step allows a mask is applied, the major objective is to filter the maximum the complex background. We use Otsu technique and edge enhancement by pyramid image in different scales. The second step is to fine detection of candidate characters by maximally stable extremal regions (MSER) based on the luminance that gives more meaning to information merged with enhanced edges and connected to surpass the limits of MSER. Then non-text components are filtered out by the character candidate classification based on DTW using SIFT and HOG features. The false positives are eliminated by geometrical properties of text blocks. Finally we apply boundary box localization after a stage of word grouping. The proposed method has been evaluated on ICDAR 2013 scene text detection competition dataset and the encouraging experiments results can be compared with the latest published algorithms.