International Journal of Pattern Recognition and Artificial Intelligence

We propose a model for the segmentation of cursive handwriting into strokes that has been derived in analogy with those proposed in the literature for early processing tasks in primate visual system. The model allows reformulating the problem of selecting on the ink the points corresponding to perceptually relevant changes of curvature as a preattentive, purely bottom-up visual task, where the conspicuity of curvature changes is measured in terms of their saliency. The modeling of the segmentation as a saliency-driven visual task has lead to a segmentation algorithm whose architecture is biologically-plausible and that does not rely on any parameter other than those that can be directly obtained from the ink. Experimental results show that the performance is very stable and predictable, thus preventing those erratic behaviors of segmentation methods often reported in the literature. They also suggest that the proposed measure of saliency has a direct relation with the dynamics of the handwriting, so as it could be used to capture in a quantitative way some aspects of cursive handwriting intuitively related to the notion of style.

In the framework of online cursive handwriting recognition, we present an efficient method for reordering the sequence of strokes composing handwriting in two special cases of interest: the horizontal bar of the character "" and the dot of the character "". The proposed method exploits shape information for selecting the strokes that most likely correspond to the features of interest, and layout and topological information for locating the strokes representing the body of the characters to which the features belong to. The method does not depend on the specific algorithm used for detecting the elementary strokes in which the electronic ink may be decomposed into. The performance of our method, evaluated on a data set of cursive words produced by 50 different writers, has shown a correct reordering of the sequence in more than 85% of the cases. Thus, the proposed method allows obtaining a more stable and invariant description of the electronic ink in terms of elementary stroke sequences, and therefore can be helpfully used as a preprocessing step for both segmentation-based and word-based handwriting recognition systems.

This paper describes a method for separating online handwritten patterns into Japanese text, figures and mathematical formulas. Today, Tablet PCs and electronic whiteboards provide much larger writing area for pen interfaces unlike PDAs (Personal Digital Assistants), through which users can easily input text, write mathematical formulas and draw figures on the screen. The fact that these objects can be written by a single pen (marker) without switching the device, mode, software or whatever else, and without any writing restrictions such as grids or boxes is one of the most important benefits of the pen interfaces. However, the task of segmenting these objects is challenging. To address this issue, we have applied a probabilistic model employing stroke features, stroke crossings and stroke densities. Further, we partially apply the approach of segmentation by recognition. Although the current recognizer for formulas is not a true recognizer, we have achieved about 81% correct segmentation for all the strokes when applied to our newly prepared database of mixed patterns. This method has been compared with a neural-network. The results show that our method is generally better but less effective in distinguishing figures from other components.

It is well known that the approach of functions of time to represent the dynamic and static characteristics of signatures usually outperforms the approaches based on parameters; in addition to this result, we propose here that the model or prototype function of the discriminant features of the signatures should be created considering the signature verification problem as a bi-objective optimization problem in which the false acceptance and false rejection rates are minimized simultaneously; to accomplish these goals, a discrete space of solutions is searched by a genetic algorithm, and a continuous space of solutions is searched by a modified gradient method, both spaces containing candidate prototype functions and the one that best meets some optimization criteria is first chosen as the optimal prototype function and then improved. Given that creating the prototype functions of features is just one of the earlier steps of a signature verification system, we also propose here a scheme of signature verification algorithm with intelligent classification. Our approach was tested in the context of random and highly skilled forgeries, with error rates below 0.1% over 7,300 verifications. Our database consisted of 1,762 exemplars, containing genuine signatures and skilled forgeries from 36 persons. Comparison to other methods of making prototype functions of features is shown.

A few years ago, a Kinematic Theory was proposed to analyze rapid human movements. The theory is based on a delta-lognormal equation which can be used to globally describe the basic properties of velocity profiles using seven parameters. This realistic model has been of great use to solve pattern recognition problems (signature verification, handwriting analysis and segmentation, etc.). To go further in that direction, a better understanding of the model is a prerequisite. This can be either in the context of psychophysical studies involving human subjects or in the context of computer simulations. In this paper, we use the same model form to develop a simulator that generates human-like velocity profiles. A basic subsystem model is both proposed and constructed with a Simulink Matlab tool; then many of these are connected to create an artificial neuromuscular network. Combining two networks in parallel, one agonist and the other antagonist, a synergy simulator is constructed. The similarity of the velocity patterns produced by the simulator is analyzed using a delta-lognormal parameter extractor. It is shown that the parameters extracted from artificially generated profiles vary in the same intervals as those of experimental profiles produced by human subjects. In future works the simulator tool will be used to study the control of rapid human movements.

Given the proliferation of touch-sensitive screen technologies, the factors contributing to efficiency of cursor control device were examined to better inform choice and design of Graphical User Interfaces. Fitts' law can be used to describe the relative efficiency of these cursor control devices. Experiment One required 18 participants to move a cursor between targets depicted upon a WACOM PL400 display graphics tablet, over various amplitudes, using a computer mouse or stylus, examining the speed and accuracy of cursor placement. The touch sensitive screen allowed faster acquisition of targets than the mouse. Since the mouse has a higher control/display ratio (gain) than the touch sensitive screen, Experiment Two addressed manipulation of the gain settings on computer mouse performance. Low gain offered some accuracy benefits. Possible posture problems associated with the touch sensitive screen were explored in Experiment Three. Screen orientation had little effect upon cursor positioning performance, suggesting the benefits of the touch sensitive screen can be transferred to user-friendly postures.

The aim of this paper is to assess the evolution in writing performance amongst typical pupils in primary education. More precisely, we propose ways of discovering groups of pupils sharing the same writing strategies during their primary education and methods for the temporal modeling of these pupils' writing strategies. For this purpose, online acquisition of writing and drawing tests have been performed three times during a period of one year for the same pupils under the same experimental conditions. A first approach, based on clustering, is applied to highlight clusters on a set of dynamic primitives chosen by an expert in the field of child development psychology. Results are presented by means of a comparative study between features of each group and writing tests. An analysis of within and between-strategies migration of pupils over time is also conducted to highlight pupils who change (or fail to change) their writing strategies during this period of one year. A second approach is used to model the problem by means of a probabilistic graphical model, i.e. a bayesian network. Expert knowledge partially determines the bayesian network structure, in which the writing strategy is represented by a hidden variable whose cardinality is estimated by the results of the clustering approach. By considering that each writing test is represented by its own (local) strategy and that there exists a global strategy which deals with each local strategy, we propose a Global Hierarchical Model. The results of our hierarchical model structured using real data highlight, among others, two global strategies that correspond to normo-writer pupils and more advanced normo-writers. A longitudinal and temporal study of the evolution of the pupils in these strategies shows that these two strategies are consistent.

The reported work aims to objectively and accurately assess the post-stroke clinical condition of visuo-spatial neglect using a series of standardized geometric shape drawing tasks. We present a method implementing existing pencil-and-paper diagnostic methods and define a set of static and dynamic features that can be extracted from drawing responses captured online using a graphics tablet. We also present a method for automatically assessing the constructional sequence of the drawing using Hidden Markov Models. The method enables the automated extraction, position identification and drawing order of individual sides of a shape within a drawing. Discrimination between two populations (a neglect population and stroke subjects without neglect as determined by existing standard assessment methods) using a combination of performance features and constructional sequence is examined across three separate drawing tasks. Results from experimentation show how a combination of sequence and performance features is able to generalize across a wide variety of input samples and obtain a diagnostic classification which can be used alongside other forms of conventional assessment. Furthermore, the application of a multi-classifier combination strategy leads to a significant increase in recognition ability.

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing.

A perturbation model for the generation of synthetic textlines from existing cursively handwritten lines of text produced by human writers is presented. The goal of synthetic textline generation is to improve the performance of an offline cursive handwriting recognition system by providing it with additional training data. It can be expected that by adding synthetic training data the variability of the training set improves, which leads to a higher recognition rate. On the other hand, synthetic training data may bias a recognizer towards unnatural handwriting styles, which could lead to a deterioration of the recognition rate. In this paper the proposed perturbation model is evaluated under several experimental conditions, and it is shown that significant improvement of the recognition performance is possible even when the original training set is large and the textlines are provided by a large number of different writers.

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. Recently, a number of classifier creation methods, known as ensemble methods, have been proposed in the field of machine learning. It has been shown that these methods are able to substantially improve recognition performance in complex classification tasks. In this paper we examine the influence of the vocabulary size and the number of training samples on the performance of three ensemble methods in the context of handwritten word recognition. The experiments were conducted with two different offline hidden Markov model based handwritten word recognizers.

The study investigates the perceptual feature similarity between different lexicons based on visual perception of the words and their representation through an observation sequence. We confirm that it is possible to use databases, which are similar in terms of morphological/perceptual features to improve the recognition performance. In this work, we demonstrated through experimentation, that it is possible to improve the recognition rate of handwritten Portuguese words by adding samples of French words in the training set. Experimental results show the efficiency of this strategy reducing the error rate.

This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78% and 93% for verification and identification, respectively. This method was successfully employed in our previous theory of individuality of handwriting developed at CEDAR — based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verification and identification as offline handwriting verification and identification tasks respectively, we depict a mapping from the handwriting domain to the signature domain.

We investigate the detection of handwriting forged by novices. To facilitate document examination it is important to develop an automated system to identify forgeries, or at least to identify those handwritings that are likely to be forged. Because forgers often carefully copy or trace genuine handwriting, we hypothesize that good forgeries — those that retain the shape and size of genuine writing — are usually written more slowly and are therefore wrinklier (less smooth) than genuine writing. From online handwriting samples we find that the writing speed of the good forgeries is significantly slower than that of the genuine writings. From corresponding offline samples we find that the wrinkliness of the good forgeries is significantly greater than that of the genuine writings, showing that this feature can help identify candidate forgeries from scanned documents. Using a total of eight handwriting distance features, including the wrinkliness feature, we train a neural network to achieved 89% accuracy on detecting forged handwriting on test samples from ten writers.