A novel post-processing method for street text recognition using gps information and string alignment

It is important to recognize text in natural images containing signs. Recognized text from natural images presents valuable information which can be used for translation, location-based services and image searches. As street view services are becoming important for major IT companies, this street text recognition is an important problem to solve. There are a lot of previous studies on street text recognition for English, Chinese and mixed languages. Although a lot of creative recognition methods have been proposed and experimented with, the average recognition rate is around 60~80%. This paper starts out with the question of how we can recognize street text appearing in a general street view with high reliability. We proposed two main tools to tackle this problem: one is spatial GPS information, and the other is string alignment which is a good method to handle the approximated string match. Since the text recognized by the previous methods can be considered a sort of approximated string from the original text(true string), the string alignment score could give a good candidate set of street text. For a group of street texts collected by string alignment, we then exploit the spatial information which can be obtained by GPS information. Our approach guarantees more than 90% recognition rate by combining alignment scores and GPS information, which is a marked improvement above the 60% rate of previous methods. Since most digital maps such as Google maps provide a precise GPS information and true textual information for a designated position, our approach can be automated to tag building information over the street view images.