A CONTENT DISCLOSURE AND TEXT IDENTIFICATION BASED ON ROAD-LEVEL SYMBOLISM USING TCH ALGORITHM

Abstract

Image enhancement is a process of improving the quality of image by improving its feature. Image contrast enrichment techniques have been largely studied in the past decades. Traffic sign disclosure and realization has been fully prepared for a great past. Traffic board detection and text recognition still leaving a protest in computer view due to its various group and the vast changeability of the data illustrated in them. The essential function can be to make an automated index of the traffic panels placed in a road to holding its care and to aid drivers. Then the figure are defined as a “bag of visual words” and restricted to applying support vector machines. Completely our own text detection and recognition method is tested on those images where a traffic board has been disclosure in system to naturally read and save the data details in the panels. Leading driver aid scheme could also use from text recognition for automated traffic signs and panels description. This visual appearance categorization method is a new approach for traffic panel detection in the state of the art. We propose a language model partly based on a dynamic dictionary for a limited geographical area using a reverse geocoding service. Experimental results on real images from Google Street View prove the efficiency of the proposed method and give way to using street-level images for different applications on ITS.