title: Toward integrated scene text reading.
abstract: The growth in digital camera usage combined with a worldly abundance of text has translated to a rich new era for a classic problem of pattern recognition, reading. While traditional document processing often faces challenges such as unusual fonts, noise, and unconstrained lexicons, scene text reading amplifies these challenges and introduces new ones such as motion blur, curved layouts, perspective projection, and occlusion among others. Reading scene text is a complex problem involving many details that must be handled effectively for robust, accurate results. In this work, we describe and evaluate a reading system that combines several pieces, using probabilistic methods for coarsely binarizing a given text region, identifying baselines, and jointly performing word and character segmentation during the recognition process. By using scene context to recognize several words together in a line of text, our system gives state-of-the-art performance on three difficult benchmark data sets.
authors.affiliation: Grinnell College, Grinnell.
authors.forename: Jerod J
authors.initials: JJ
authors.lastname: Weinman
authors.affiliation: Grinnell College, Grinnell.
authors.forename: Zachary
authors.initials: Z
authors.lastname: Butler
authors.affiliation: Grinnell College, Grinnell.
authors.forename: Dugan
authors.initials: D
authors.lastname: Knoll
authors.affiliation: University of Massachusetts, Amherst.
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Feild
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D004282:Documentation / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018511:Systems Integration