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

• Main Authors: SeonYeong Kim, Sung-Hwan Kim, Hwan-Gue Cho
• Format: Conference Proceeding
• Language: English
• Subjects: Character recognition; Computer science; Computers; Global Positioning System; Google; Image recognition; Text recognition
• DOI: 10.1109/CSAE.2011.5952661

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.