Cloud of Line Distribution and Random Forest Based Text Detection from Natural/Video Scene Images

Text detection in natural and video scene images is still considered to be challenging due to unpredictable nature of scene texts. This paper presents a new method based on Cloud of Line Distribution (COLD) and Random Forest Classifier for text detection in both natural and video images. The proposed method extracts unique shapes of text components by studying the relationship between dominant points such as straight or cursive over contours of text components, which is called COLD in polar domain. We consider edge components as text candidates if the edge components in Canny and Sobel of an input image share the COLD property. For each text candidate, we further study its COLD distribution at component level to extract statistical features and angle oriented features. Next, these features are fed to a random forest classifier to eliminate false text candidates, which results representatives. We then perform grouping using representatives to form text lines based on the distances between edge components in the edge image. The statistical and angle orientated features are finally extracted at word level for eliminating false positives, which results in text detection. The proposed method is tested on standard database, namely, SVT, ICDAR 2015 scene, ICDAR2013 scene and video databases, to show its effectiveness and usefulness compared with the existing methods.