handwritten word recognizer
Globally Trained Handwritten Word Recognizer using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models
We introduce a new approach for on-line recognition of handwrit(cid:173) ten 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 informa(cid:173) tion about trajectory direction and curvature. The recognizer is a convolution network which can be spatially replicated. From the network output, a hidden Markov model produces word scores.
Globally Trained Handwritten Word Recognizer using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models
Bengio, Yoshua, LeCun, Yann, Henderson, Donnie
We introduce a new approach for online 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 which 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. 1 Introduction Natural handwriting is often a mixture of different "styles", lower case printed, upper case, and cursive.
Globally Trained Handwritten Word Recognizer using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models
Bengio, Yoshua, LeCun, Yann, Henderson, Donnie
We introduce a new approach for online 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 which 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. 1 Introduction Natural handwriting is often a mixture of different "styles", lower case printed, upper case, and cursive.
Globally Trained Handwritten Word Recognizer using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models
Bengio, Yoshua, LeCun, Yann, Henderson, Donnie
We introduce a new approach for online recognition of handwritten wordswritten 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 abouttrajectory direction and curvature. The recognizer is a convolution network which can be spatially replicated. From the network output, a hidden Markov model produces word scores.