A successful handwritten word recognition (HWR) system using a variable duration hidden Markov model (VDHMM) and the path discriminant-HMM (PD-HMM) strategy is easy to implement. The central theme of the paper is to show that if the duration statistics are computed, it could be utilized to implement a model-discriminant-HMM (MD-HMM) approach for better experimental results. The paper also describes a PD-HMM based HWR system where the duration statistics are not explicitly computed, but results are still comparable to VDHMM based HWR scheme.

We describe an MD-HMM (model discriminant HMM) based handwritten word recognition (HWR) system (called NEHMM-nonergodic HMM) whose system parameters are derived from the parameters of the VDHMM (variable duration HMM) system described by Chen, Kundu and Srihari (see IEEE Trans. on Image Proc., vol.4, no.12, p.1675-88, 1995). The new HMM achieves better experimental results by a more efficient utilization of variable duration information. However, more often the problem is 'reliable computation' of duration probabilities given limited databases. A scheme (VSLHMM-variable sequence length HMM) has been presented to avoid the computation of duration probabilities altogether without sacrificing the performance gain of the VDHMM system.

In this paper, we have developed a handwritten word recognition scheme based on a single contextual hidden Markov model (HMM) incorporated with an adaptive length Viterbi algorithm. This work attempts to extend our earlier HMM scheme for naturally segmented word recognition to cursive and nonsegmented word recognition. The algorithm pre-segments the script into characters and/or fractions of characters, dynamically selects the optimal segmentation points, determines the word length, and recognizes the word according to the maximum path probability. The HMM is on top of, but independent of, script segmentation and character recognition techniques, and therefore can be further improved by incorporating more refined segmentation and character recognition procedure. The experiments have shown promising results.

The authors have developed a handwritten word recognition scheme based on a single contextual, discrete symbol probability hidden Markov model (HMM) incorporated with an adaptive length Viterbi algorithm. This work attempts to extend the earlier HMM scheme for naturally segmented word recognition to cursive and nonsegmented word recognition. The algorithm presegments the script into characters and/or fractions of characters, dynamically selects the correct segmentation points, determines the word length, and recognizes the word according to the maximum path probability. The HMM is on top of, but independent of, script segmentation and character recognition techniques, and therefore leaves room for further improvement. The experiments have shown promising results and directions for further improvement.>

A planar shape-recognition approach is presented which is based on hidden Markov models and autoregressive parameters. This approach segments closed shapes into segments and explores the characteristic relations between consecutive segments to make classification at a finer level. The algorithm can tolerate much shape contour perturbation, and a moderate amount of occlusion. The overall classification scheme is independent of shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained all over again when a new class of shapes is added.>

A planar shape recognition approach is presented which is based on a hidden Markov model and autoregressive parameters. This approach segments closed shapes into segments and explores the characteristic relations between consecutive segments to make classification at a finer level. The algorithm can tolerate a lot of shape contour perturbation and a moderate amount of occlusion. Also, the overall classification scheme is independent of shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained all over again when a new class of shapes is added.>

The authors present a planar shape recognition approach based on the hidden Markov model and autoregressive parameters. This approach segments closed shapes to make classifications at a finer level. The algorithm can tolerate a lot of shape contour perturbation and a moderate amount of occlusion. An orientation scheme is described to make the overall classification insensitive to shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained again when a new class of shapes is added.>

The handwritten word recognition problem is modeled in the framework of the hidden Markov model (HMM). The states of HMM are identified with the letters of the alphabet. The optimum symbols are then generated experimentally using 15 different features. Both the first- and second-order HMMs are proposed for the recognition tasks. Using the existing statistical knowledge of English, the calculation scheme of the model parameters are immensely simplified. Once the model is established, the Viterbi algorithm is used to recognize the sequence of letters consisting the word. Some experimental results are also provided indicating the success of the scheme.>

An extended Viterbi algorithm is presented that gives a maximum a posteriori estimation of the second-order hidden Markov process. The advantage of the second-order model and the complexity of the extended algorithm are compared with those of the original first-order one. The method used to develop the extended algorithm can also be used to extend the Viterbi algorithm further to any higher order.>