LEREC - A NN HMM HYBRID FOR ONLINE HANDWRITING RECOGNITION/

We introduce a new approach for on-line recognition of handwritten wor ds written in unconstrained mixed style. The preprocessor performs a w ord-level normalization by fitting a model of the word structure using the EM algorithm. Words are then coded into low resolution ''annotate d images'' where each pixel contains information about trajectory dire ction and curvature. The recognizer is a convolution network that can be spatially replicated. From the network output, a hidden Markov mode l produces word scores. The entire system is globally trained to minim ize word-level errors.