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This paper proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing unconstrained offline handwritten texts. The structural part of the optical models has been modeled with Markov chains, and a Multilayer Perceptron is used to estimate the emission probabilities. This paper also presents new techniques to remove slope and slant from handwritten text and to normalize the size of text images with supervised learning methods. Slope correction and size normalization are achieved by classifying local extrema of text contours with Multilayer Perceptrons. Slant is also removed in a nonuniform way by using Artificial Neural Networks. Experiments have been conducted on offline handwritten text lines from the IAM database, and the recognition rates achieved, in comparison to the ones reported in the literature, are among the best for the same task. © 2006 IEEE., The authors acknowledge the valuable help provided by Moises Pastor, Juan Miguel Vilar, Alex Graves, and Marcus Liwicki. Thanks are also due to the reviewers and the Editor-in-Chief for their many valuable comments and suggestions. This work has been partially supported by the Spanish Ministerio de Educacion y Ciencia (TIN2006-12767) and by the BPFI 06/250 Scholarship from the Conselleria d'Empresa, Universitat i Ciencia, Generalitat Valenciana.

This work presents an unconstrained offline hand-written line recognition system based on hybrid HMM (Hidden Markov Model)/ANN (Artificial Neural Network) models. The particularity of the system lies in the use of an ensemble of connectionist/statistical character n-gram language models. These language models are trained with a text corpus at character level; therefore, no explicit lexicon is used during recognition. The recognizer is thus able to output words which do not belong to that corpus. The proposed system favorably behaves compared to using a standard character n-gram on the IAM database lines corpus and achieves error rates comparable to state-of-the-art lexicon-driven alternatives.

The recognition performance of current automatic offline handwriting transcription systems is far from being perfect. This is the reason why there is a growing interest in assisted transcription systems, which are more efficient than correcting by hand an automatic transcription. A recent approach to interactive transcription involves multimodal recognition, where the user can supply an online transcription of some of the words. In this paper, a description of the bimodal engine, which entered the "Bi-modal Handwritten Text Recognition" contest organized during the 2010 ICPR, is presented. The proposed recognition system uses Hidden Markov Models hybridized with neural networks (HMM/ANN) for both offline and online input. The N-best word hypothesis scores for both the offline and the online samples are combined using a log-linear combination, achieving very satisfying results.

Our open source real-time recognition engine for on-line isolated handwritten characters is a 3-Nearest Neighbor classifier that uses approximate dynamic time warping comparisons with a set of prototypes filtered by two fast distance-based methods. This engine achieved excellent classification rates on two writer-independent tasks:UJIpenchars and Pendigits. We present the integration of multilayer perceptrons into our engine, an improvement that speeds up the recognition process by taking advantage of the independence of these networks’ classification times from training set sizes. We also present experimental results on our new publicly available UJIpenchars2 database and on Pendigits.

In this paper we propose a family of Viterbi algorithms specialized for lexical tree based FSA and HMM acoustic models. Two algorithms to decode a tree lexicon with left-to-right models with or without skips and other algorithm which takes a directed acyclic graph as input and performs error correcting decoding are presented. They store the set of active states topologically sorted in contiguous memory queues. The number of basic operations needed to update each hypothesis is reduced and also more locality in memory is obtained reducing the expected number of cache misses and achieving a speed-up over other implementations.

The goal of this work is to present an efficient implementation of the Backpropagation (BP) algorithm to train Artificial Neural Networks with general feedforward topology. This will lead us to the "consecutive retrieval problem" that studies how to arrange efficiently sets into a sequence so that every set appears contiguously in the sequence. The BP implementation is analyzed, comparing efficiency results with another similar tool. Together with the BP implementation, the data description and manipulation features of our toolkit facilitates the development of experiments in numerous fields.