title: LeRec: a NN/HMM hybrid for on-line handwriting recognition.
abstract: We introduce a new approach for on-line recognition of handwritten words written in unconstrained mixed style. The preprocessor performs a word-level normalization by fitting a model of the word structure using the EM algorithm. Words are then coded into low resolution "annotated images" where each pixel contains information about trajectory direction and curvature. The recognizer is a convolution network that can be spatially replicated. From the network output, a hidden Markov model produces word scores. The entire system is globally trained to minimize word-level errors.
authors.affiliation: AT&T Bell Laboratories, Holmdel, NJ 07733, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Bengio
authors.forename: Y
authors.initials: Y
authors.lastname: LeCun
authors.forename: C
authors.initials: C
authors.lastname: Nohl
authors.forename: C
authors.initials: C
authors.lastname: Burges
mesh_terms: D000465:Algorithms; D006236:Handwriting; D006801:Humans; D008390:Markov Chains; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results