Abstract-Recognizing text in images taken in the wild is a challenging problem that has received great attention in recent years. Previous methods addressed this problem by first detecting individual characters, and then forming them into words. Such approaches often suffer from weak character detections, due to large intra-class variations, even more so than characters from scanned documents. We take a different view of the problem and present a holistic word recognition framework. In this, we first represent the scene text image and synthetic images generated from lexicon words using gradient-based features. We then recognize the text in the image by matching the scene and synthetic image features with our novel weighted Dynamic Time Warping (wDTW) approach.
In one of our previous works REF , we compared features computed on the entire scene text image and those generated from synthetic font renderings of lexicon words with a novel weighted dynamic time warping (wDTW) approach to recognize words.
Whole is Greater than Sum of Parts: Recognizing Scene Text Words
2013 12th International Conference on Document Analysis and Recognition
2013 12th International Conference on Document Analysis and Recognition
Computer Science