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 knowledge-based segmentation system for handwritten dates on bank cheques

Abstract: Segmenting handwritten date fields on bank cheque images into three subimages corresponding to the day, month and year is the first and critical step of our date recognition system. The paper describes a knowledge-based segmentation system, which introduces different kinds of knowledge at different segmentation stages to improve the performance. The knowledge includes information on the writing style, syntactic and semantic constraints, etc. Results have shown that the system is very effective compared with a previous structural feature based method.

A comparison of methods of extracting curvature features

Abstract: Examines the extraction of curvature features from the contours of 2D objects. General schemes for corner detection and particular methods from the recent OCR literature are considered. Eight approaches are compared with respect to their results on a set of 100 handwritten numerals of varying styles and sizes. Strengths and weaknesses are summarized for each method. >

Multi-class SVM based gradient feature for banknote recognition

Abstract: Banknote recognition system is the focus of different image processing and pattern recognition research. With the improvement in modern-day banking operations, automated systems for banknote recognition have become pertinent. Recognition of banknotes is a challenging task as banknotes can suffer from defects and images get distorted during acquisition, which raises the need for a robust recognition system to mitigate these flaws. This research proposes a new banknote recognition approach that classifies the principal components of the extracted Histogram of Gradient feature vectors using an efficient error correcting output code technique based on a Multi-Class Support Vector Machine. The method works on both sides of the bank note and efficiently recognize the denomination based on any side of the bill. The system was implemented using the Nigerian Naira, and for experimental evaluation, additional analysis was conducted using the US Dollar, Canadian Dollar, and Euro banknotes. Finally, the system performance was evaluated based on the recognition rate and processing time.

A multi-net local learning framework for pattern recognition

Abstract: This paper proposes a general local learning framework to effectively alleviate the complexities of classifier design by means of "divide and conquer" principle and ensemble method. The learning framework consists of quantization layer and ensemble layer. After GLVQ and MLP are applied to the framework, the proposed method is tested on MNIST handwritten digit database. The obtained performance is very promising, an error rate with 0.99%, which is comparable to that of LeNet5, one of the best classifiers on this database. Further, in contrast to LeNet5, our method is especially suitable for a large-scale real-world classification problem.

Iris segmentation using game theory

Abstract: Robust segmentation of an iris image plays an important role in iris recognition. However, the nonlinear deformations, pupil dilations, head rotations, motion blurs, reflections, nonuniform intensities, low image contrast, camera angles and diffusions, and presence of eyelids and eyelashes often hamper the conventional iris/pupil localization methods, which utilize the region-based or the gradient-based boundary-finding information. The novelty of this research effort is that we describe a new iris segmentation scheme using game theory to elicit iris/pupil boundaries from a nonideal iris image. We apply a parallel game-theoretic decision making procedure by modifying Chakraborty and Duncan’s algorithm, which integrates (1) the region-based segmentation and gradient-based boundary-finding methods and (2) fuses the complementary strengths of each of these individual methods. This integrated scheme forms a unified approach, which is robust to noise and poor localization, and less affected by weak iris/sclera boundaries. The verification and identification performance of the proposed method are validated using the ICE 2005, the UBIRIS Version 1, WVU Nonideal, and the CASIA Version 3 data sets.

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.