Reviews: Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data

Method and Novelty: The authors present a model that has a number of strengths. First, the character-level model is trained on synthetically generated images from a font library, independently of the training corpus. Second, the model converts each training image into a factor graph and learns the spatial relationships between landmarks in each character. This model can readily assign a probability to each candidate character for an image, and the authors provide a description of a two-stage inference algorithm that consists of approximate belief propagation followed by refinement via a backtracking procedure. The candidate characters are then supplied to a word model, which is a fairly standard structured prediction using bigram and trigram features.