Based on a study of the extensive literature in handprint recognition, this paper presents a survey in this challenging field. Recognition algorithms, data bases, character models, and handprint standards are examined. Achievements in the recognition of handprinted numerals, alphanumerics, Fortran, and Katakana characters are analyzed and compared. Data quality and constraints, as well as human and machine factors are also described. Characteristics, problems, and actual results on on-line recognition of handprinted characters for different applications are discussed. New emphases and directions are suggested.

Automatic recognition of handprinted characters&#8212;The state of the art

There is significant interest in battery-powered sensor networks to be used for detection in a wide variety of applications, from surveillance and security to health and environmental monitoring. Severe energy and bandwidth constraints at each sensor node demand system-level approaches to design that consider detection performance jointly with system-resource constraints. Our approach is to formulate detection problems with constraints on the expected cost arising from transmission (sensor nodes to a fusion node) and measurement (at each sensor node) to address some of the system-level costs in a sensor network. For a given resource constraint, we find that randomization over the choice of measurement and over the choice of when to transmit achieves the best performance (in a Bayesian, Neyman-Pearson, and Ali-Silvey sense). To facilitate design, we describe performance criteria in the send/no-send transmission scenario, where the joint optimization over the sensor nodes decouples into optimization at each sensor node.

/pdf/energy-efficient-detection-in-sensor-networks-257ax0sy0z.pdf

Energy-efficient detection in sensor networks

The only guaranteed technique for choosing the best subset of N properties from a set of M is to try all (MN) possible combinations. This is computationally impractical for sets of even moderate size, so heuristic techniques are required. This paper presents seven techniques for choosing good subsets of properties and compares their performance on a nine-class vectorcardiogram classification problem.

A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties

The basic concept of learning control is introduced. The following five learning schemes are briefly reviewed: 1) trainable controllers using pattern classifiers, 2) reinforcement learning control systems, 3) Bayesian estimation, 4) stochastic approximation, and 5) stochastic automata models. Potential applications and problems for further research in learning control are outlined.

https://commons.erau.edu/cgi/viewcontent.cgi?article=2975&context=space-congress-proceedings

Learning control systems--Review and outlook

/pdf/learning-control-systems-review-and-outlook-22fdm01tki.pdf

Learning Control Systems-Review and Outlook

This paper presents the dynamic programming approach to the design of optimal pattern recognition systems when the costs of feature measurements describing the pattern samples are of considerable importance. A multistage or sequential pattern classifier which requires, on the average, a substantially smaller number of feature measurements than that required by an equally reliable nonsequential classifier is defined and constructed through the method of recursive optimization. Two methods of reducing the dimensionality in computation are presented for the cases where the observed feature measurements are 1) statistically independent, and 2) Markov dependent. Both models, in general, provide a ready solution to the optimal sequential classification problem. A generalization in the design of optimal classifiers capable of selecting a best sequence of feature measurements is also discussed. Computer simulated experiments in character recognition are shown to illustrate the feasibility of this approach.

A Dynamic Programming Approach to Sequential Pattern Recognition

—In this note, three suboptimal solutions are obtained for the joint sequential feature selection and pattern classification problem. These solutions allow the comparison of two distinctly different approximations to the optimal procedure. One approximation involves simplifying assumptions on the underlying distribution of features for each pattern class, while the second involves an approximation in the implementation of the optimal procedure.

On Suboptimal Sequential Pattern Recognition

https://ieeexplore.ieee.org/iel5/9/24098/01098765.pdf

Gerald P. Cardillo

Papers

A Dynamic Programming Approach to Sequential Pattern Recognition

A Dynamic Programming Approach to Sequential Pattern Recognition

On Suboptimal Sequential Pattern Recognition

Divergence and linear classifiers for feature selection