A Machine Learning Approach to Hypothesis Decoding in Scene Text Recognition

Scene Text Recognition (STR) is a task of localizing and transcribing textual information captured in real-word images. With its increasing accuracy, it becomes a new source of textual data for standard Natural Language Processing tasks and poses new problems because of the specific nature of Scene Text. In this paper, we learn a string hypotheses decoding procedure in an STR pipeline using structured prediction methods that proved to be useful in automatic Speech Recognition and Machine Translation. The model allow to employ a wide range of typographical and language features into the decoding process. The proposed method is evaluated on a standard dataset and improves both character and word recognition performance over the baseline.