Ching Y. Suen

Bio: Ching Y. Suen is an academic researcher from Concordia University. The author has contributed to research in topics: Handwriting recognition & Feature extraction. The author has an hindex of 65, co-authored 511 publications receiving 23594 citations. Previous affiliations of Ching Y. Suen include École de technologie supérieure & Concordia University Wisconsin.

A syntax directed level building algorithm for large vocabulary handwritten word recognition

Abstract: This paper describes a large vocabulary handwritten word recognition system based on a syntax{directed level building algorithm (SDLBA) that incorporates contextual information. The sequences of observations extracted from the input images are matched against the entries of a tree{structure lexicon where each node is represented by a 10{state character HMM. The search proceeds breadth| rst and each node is decoded by the SDLBA. Contextual information about writing styles and case transitions is injected between the levels of the SDLBA. An implementation of the SDLBA together with a 36,100{entry lexicon is described. In terms of recognition speed, the results show that the SDLBA together with the tree{structured lexicon outperforms a baseline system that uses a Viterbi{ at{lexicon scheme while maintaining the same accuracy and consuming a reasonable amount of memory.

Binary Decision Tree Using Genetic Algorithm for Recognizing Defect Patterns of Cold Mill Strip

Abstract: This paper presents a method to recognize the various defect patterns of a cold mill strip using a binary decision tree constructed by genetic algorithm(GA). In this paper, GA was used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are divided into two classes using a linear decision function. In this way, the classifier using the binary decision tree can be constructed automatically, and the final recognizer is implemented by a neural network trained by standard patterns at each node. Experimental results are given to demonstrate the usefulness of the proposed scheme.

Building a perception based model for reading cursive script

Abstract: This paper presents a new perception based model for reading cursive script. We describe the organization of our pseudo-neuronal system and show the role of activation mechanism in perceiving and reading cursive script. We have introduced into our model some characteristics specific to cursive script. First, we use more appropriate features such as ascenders and descenders. Second, we deal with the ambiguity of letter location by introducing the concept of the fuzzy position. The location as well as the missing letters are deduced from the context (i.e. the word-letter lexicon). After implementation of our method, preliminary qualitative results have been obtained and are discussed. We are concentrating now on further formalizing and generalizing the proposed model on a larger data base.

Splitting-shooting methods for nonlinear transformations of digitized patterns

Abstract: New splitting-shooting methods are presented for nonlinear transformations T: ( xi , eta ) to (x,y) where x=x( xi , eta ), y=y( xi , eta ). These transformations are important in computer vision, image processing, pattern recognition, and shape transformations in computer graphics. The methods can eliminate superfluous holes or blanks, leading to better images while requiring only modest computer storage and CPU time. The implementation of the proposed algorithms is simple and straightforward. Moreover, these methods can be extended to images with gray levels, to color images, and to three dimensions. They can also be implemented on parallel computers or VLSI circuits. A theoretical analysis proving the convergence of the algorithms and providing error bounds for the resulting images is presented. The complexity of the algorithms is linear. Graphical and numerical experiments are presented to verify the analytical results and to demonstrate the effectiveness of the methods. >

Splitting-integrating method for normalizing images by inverse transformations

Abstract: The splitting-integrating method is a technique developed for the normalization of images by inverse transformation. It does not require solving nonlinear algebraic equations and is much simpler than any existing algorithm for the inverse nonlinear transformation. Moreover, its solutions have a high order of convergence, and the images obtained through T/sup -1/ are free from superfluous holes and blanks, which often occur in transforming digitized images by other approaches. Application of the splitting-integrating method can be extended to supersampling in computer graphics, such as picture transformations by antialiasing, inverse nonlinear mapping, etc. >

Gradient-based learning applied to document recognition

Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Statistical pattern recognition: a review

Abstract: The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

On combining classifiers

Abstract: We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions-the sum rule-outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.

Pattern recognition and neural networks

Abstract: From the Publisher: Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.