Automatic detection and recognition of road signs is an important component of automated driver assistance systems contributing to the safety of the drivers, pedestrians and vehicles. Despite significant research, the problem of detecting and recognizing road signs still remains challenging due to varying lighting conditions, complex backgrounds and different viewing angles. We present an effective and efficient method for detection and recognition of traffic signs from images. Detection is carried out by performing color based segmentation followed by application of Hough transform to find circles, triangles or rectangles. Recognition is carried out using three state-of-the-art feature matching techniques, SIFT, SURF and BRISK. The proposed system evaluated on a custom developed dataset reported promising detection and recognition results. A comparative analysis of the three descriptors reveal that while SIFT achieves the best recognition rates, BRISK is the most efficient of the three descriptors in terms of computation time.

Abstract-This work presents a system for extraction and script identification of multilingual artificial text appearing in video images. As opposed to most of the existing text extraction systems which target textual occurrences in a particular script or language, we have proposed a generic multilingual text extraction system that relies on a combination of unsupervised and supervised techniques. The unsupervised approach is based on application of image analysis techniques which exploit the contrast, alignment and geometrical properties of text and identify candidate text regions in an image. Potential text regions are then validated by an Artificial Neural Network (ANN) using a set of features computed from Gray Level Co-occurrence Matrices (GLCM). The script of the extracted text is finally identified using texture features based on Local Binary Patterns (LBP). The proposed system was evaluated on video images containing textual occurrences in five different languages including English, Urdu, Hindi, Chinese and Arabic. The promising results of the experimental evaluations validate the effectiveness of the proposed system for text extraction and script identification.

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