Adaptation de modèles de Markov cachés - Application à la reconnaissance de caractères imprimés

International audienceWe present in this paper a new algorithm for the adaptation of hidden Markov models (HMM models). The principle of our iterative adaptive algorithm is to alternate an HMM structure adaptation stage with an HMM Gaussian MAP adaptation stage. This algorithm is applied to the recognition of printed characters to adapt the models learned by a polyfont character recognition engine to new forms of characters. Comparing the results with those of MAP and MLLR classic adaptations shows a slight increase in the performance of the recognition system

Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition

In this work we present a framework for the recognition of natural scene text. Our framework does not require any human-labelled data, and performs word recognition on the whole image holistically, departing from the character based recognition systems of the past. The deep neural network models at the centre of this framework are trained solely on data produced by a synthetic text generation engine -- synthetic data that is highly realistic and sufficient to replace real data, giving us infinite amounts of training data. This excess of data exposes new possibilities for word recognition models, and here we consider three models, each one "reading" words in a different way: via 90k-way dictionary encoding, character sequence encoding, and bag-of-N-grams encoding. In the scenarios of language based and completely unconstrained text recognition we greatly improve upon state-of-the-art performance on standard datasets, using our fast, simple machinery and requiring zero data-acquisition costs

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field