A stochastic model of stroke order variation is proposed and applied to the stroke order free online Kanji character recognition.The proposed model is a hidden Markov model (HMM) with a special topology to represent all stroke order variations. A sequence of state transitions from the initial state to the final state of the model represents one stroke order and provides a probability of the stroke order.The distribution of the stroke order probability can be trained automatically by using an EM algorithm from a training set of on-line character patterns. Experimental results on large-scale test patterns showed that the proposed model could represent actual stroke order variations appropriately and improve recognition accuracy by penalizing incorrect stroke orders.

A new hidden Markov model (HMM) is proposed for on-line character recognition using two typical features, pen-direction feature and pen-coordinate feature. These two features are quite different in their stationarity; pen-direction feature is stationary within every line segment of a stroke whereas pen-coordinate feature is not. In the proposed HMM, these contrasting features are used in a separative and selective way. Specifically speaking, pen-direction feature is out putted repeatedly at intra-state transition whereas pen-coordinate feature is out putted once at inter-state transition. The superiority of the proposed HMM over the conventional HMMs was shown through single-stroke and multi-stroke character recognition experiments.

For on-line character recognition, predictive DP match- ing is proposed where two physically different features, co- ordinate features and directional features, are handled in a unified manner. For this unification, the distance of the directional features is converted into a distance of the co- ordinate features by a feature prediction technique. An ex- perimental result showed that the predictive DP matching could attain a recognition rate comparable to the rate by the conventional DP matching which requires the costly op- timization of the weight to balance the two features.

This paper proposes a novel dewarping technique for document images of bound volumes. This technique is a kind of model fitting techniques for estimating the warp of each text line by fitting some elastic curve model to the text line. Differing from conventional techniques, the proposed technique is applicable to document images including local irregularities such as formulae, short text lines, and figures, since the proposed technique dewarps whole document images by fitting splines while considering the global optimality that specifies the desirable relationship among the splines. The experimental results on several document images including the local irregularities indicated the effectiveness of the proposed technique. The experimental result also indicated the effectiveness of the vertical division of a document image into some partial document images for more accurate dewarping.

We try to link elastic matching with a statistical discrimination framework to overcome the overfitting problem which often degrades the performance of elastic matching-based online character recognizers. In the proposed technique, elastic matching is used just as an extractor of a feature vector representing the difference between input and reference patterns. Then quadratic discrimination is performed under the assumption that the feature vector is governed by a Gaussian distribution. The result of a recognition experiment on UNIPEN database (Train-R01/V07, 1a) showed that the proposed technique can attain a high recognition rate (97.95%) and outperforms a recent elastic matching-based recognizer.

In this paper, a mosaicing-by-recognition technique is proposed where video mosaicing and text recognition are simultaneously and collaboratively optimized in a one-step manner. Specifically, multiple frames capturing a long text line are optimally concatenated with a guide of the text recognition framework. In this optimization process, rotation, scaling, vertical shift, and speed fluctuation, which often appear in video frames captured by hand-held cameras, are compensated. The optimization is performed by a DP-based algorithm. The results of experiments to evaluate not only the accuracy of text recognition but also that of video mosaicing indicate that the proposed technique is practical and can provide reasonable results in most cases.

An on-line handwritten character recognition technique based on a new HMM is proposed. In the proposed HMM, not only pen-direction feature but also pen-coordinate feature are separately utilized for describing the shape variation of on-line characters accurately. Specifically speaking, the proposed HMM outputs a pen-coordinate feature at each inter-state transition and outputs a pen-direction feature at each intra-state transition, i.e., self-transition. Thus, each state of the proposed HMM can specify the starting position and the direction of a line segment by its incoming inter-state transition and intra-state transition, respectively. The results of recognition experiments on 10-stroke Chinese characters show that the proposed HMM outperforms the conventional HMM which does not use the pen-coordinate feature because of its non-stationarity.

For handwritten character recognition, a new elastic image matching (EM) technique based on a class-dependent deformation model is proposed. In the deformation model, any deformation of a class is described by a linear combination of eigen-deformations, which are intrinsic deformation directions of the class. The eigen-deformations can be estimated statistically from the actual deformations of handwritten characters. Experimental results show that the proposed technique can attain higher recognition rates than conventional EM techniques based on class-independent deformation models. The results also show the superiority of the proposed technique over those conventional EM techniques in computational efficiency.

The effectiveness of piecewise linear 2D warping, a dynamic programming-based elastic image matching technique, in handwritten character recognition is investigated. The technique presented is capable of providing compensation for most variations in character patterns with tractable computation. The superiority of the present technique over several conventional 2D warping techniques in variation compensation is experimentally justified. Another comparison with monotonic and continuous 2D warping, a more flexible matching technique, reveals that the method presented takes far less computation than the latter, yet provides almost the same recognition accuracy for most categories.

Slant correction is an indispensable technique for handwritten word recognition systems. Conventional slant correction techniques estimate the average slant angle of component characters and then correct the slant uniformly. Thus these conventional techniques will perform successfully under the assumption that each word is written with a constant slant. However, it is more widely acceptable assumption that the slant angle fluctuates during writing a word. In this paper, a nonuniform slant correction technique is presented where the slant correction problem is formulated as an optimal estimation problem of local slant angles at all horizontal positions. The optimal estimation is governed by a criterion function and several constraints for the global and local validity of the local angles. The optimal local slant angles which maximize the criterion satisfying the constraints are searched for efficiently by a dynamic programming based algorithm. Experimental results show the advantageous characteristics of the present technique over the uniform slant correction techniques.

An efficient dynamic programming (DP) algorithm for 2D elastic matching is proposed. The present DP algorithm requires far less complexity than previous DP-based elastic matching algorithms. This complexity reduction results from piecewise linearization of a 2D-2D mapping which specifies an elastic matching between two given images. Since this linearization can be guided by a priori knowledge related to image patterns to be matched, the present DP algorithm often provides sufficient matching as is shown by experimental results.

In this paper, a handwritten character recognition experiment using a monotonic and continuous two-dimensional warping algorithm is reported. This warping algorithm is based on dynamic programming and searches for the optimal pixel-to-pixel mapping between given two images subject to two-dimensional monotonicity and continuity constraints. Experimental comparisons with rigid matching and local perturbation show the performance superiority of the monotonic and continuous warping in character recognition.

A two-dimensional warping algorithm is presented which searches for the optimal pixel mapping subject to continuity and monotonicity constraints. These constraints enable us to preserve topological structure in images. The search algorithm is based on dynamic programming (DP). As implementation techniques, acceleration by beam search and excessive warp suppression by penalty and/or range limitation are investigated. Experimental results show that this method provides successful warpings between images.

A description is given of speaker-independent word recognition based on a new neural network model called the dynamic programming neural network (DNN), which can treat time-sequence patterns. DNN is based on the integration of a multilayer neural network and dynamic-programming-based matching. Speaker-independent isolated Japanese digit recognition experiments were carried out using data uttered by 107 speakers (50 speakers for training and 57 speakers for testing). The recognition accuracy was 99.3%, suggesting that the model can be effective for speech recognition.>

This paper proposes two methods that cope with difficulties in connected digit recognition. One is the 'Demi-Word Pair Reference Pattern(DWPR) Method', which is useful for dealing with the coarticulation effect appearing at the word boundaries. The other is the 'Typically Distorted Pattern (TDP) Method'. This helps prepare robust reference templates including several types of distortion which often occur in natural speech. An experiment has been performed for 20 untrained speakers in speaker-dependent mode. The TDP was gathered from multiple speakers' pattern. The results showed that the TDP+DWPR integrated method achieved considerably higher recognition accuracy (99.0%) than a conventional word-based DP-matching method (97.5%).

A new single-chip microprocessor for speech recognition, the SRP, has been developed, utilizing a multiprocessor architecture and a pipelined structure. It can recognize up to 340 isolated words or 40 connected words in real time. The SRP contains a vector distance calculator, a DP-equation calculator, and an I/O controller operating in a pipelined manner. Algorithm variations and operation parameters are user programmable, and the total size of the SRP program for a typical speech recognition system is about 700 words. The device has been fabricated with n-channel Si-gate E/D MOS technology with 2.5 μm design rules and employs 7296 three-transistor dynamic RAM cells for a total of more than 40 000 transistors.

A new single-chip microprocessor for speech recognition has been developed utilizing multi-processor architecture and pipelined structure. By DP-matching algorithm, the processor recognizes up to 340 isolated words or 40 connected words in realtime.