Scene text recognition has gained significant attention from the computer vision community in recent years. Often images contain text which gives rich and useful information about their content. Recognizing such text is a challenging problem, even more so than the recognition of scanned documents. Given the rapid growth of camera-based applications readily available on mobile phones, understanding scene text is more important than ever. One could, for instance, foresee an application to answer questions such as, “What does this sign say?”. This is related to the problem of Optical Character Recognition (OCR), which has a long history in the computer vision community. However, the success of OCR systems is largely restricted to text from scanned documents. Scene text exhibits a large variability in appearances, and can prove to be challenging even for the state-of-the-art OCR methods. Many scene understanding methods recognize objects and regions like roads, trees, sky etc in the image successfully, but tend to ignore the text on the sign board. Our goal is to fill this gap in understanding the scene.

   
• Both Lexicon driven and Lexicon free scene text recognition
• Uses both Top-down (lexicons) and Bottom-up cues (character detection)
• Infers the word with a joint inference in CRF
• Seamless integration of higher order priors into CRF
• State-of-the-art results on public datasets
• Novel word recognition dataset with 5K words ( IIIT 5K-word dataset)