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.

Particle Swarm Optimization with Random Forests for Handwritten Arabic Recognition System

Abstract: There are many problems facing the processing of a handwritten Arabic recognition system such as unlimited variation in human handwriting, similarities of character shapes and their position in the word. This paper presents a handwritten Arabic character recognition system based on Particle Swarm Optimization with random Forests. The main objective of the proposed system is to improve the recognition rate and reduce the feature set size. The proposed system is trained and tested by a well-known classifier; Random forests (RF) on CENPRMI dataset. The proposed optimization algorithm obtained promising results in terms of classification accuracy as the proposed system is able to recognize 91.66 % of our test set correctly, as well as, it reduced the computational time. When comparing our results with other related works we find that our results is the highest among other published results.

Multi-feature Selection of Handwriting for Gender Identification Using Mutual Information

Abstract: This paper presents a new flexible approach to predict the gender of the writers from their handwriting samples. Handwriting features can be extracted from different methods. Therefore, the multi-feature sets are irrelevant and redundant. The conflict of the features exists in the sets, which affects the accuracy of classification and the computing cost. This paper proposes a Mutual Information (MI) approach, that focuses on feature selection. The approach can decrease redundancies and conflicts. In addition, it extracts an optimal subset of features from the writing samples produced by male and female writers. The classification is carried out using a Support Vector Machine (SVM) on two databases. The first database comes from the ICDAR 2013 competition on gender prediction, the other database contains the Registration-Document-Form (RDF) database in Chinese. The proposed and compared methods were evaluated on both databases. Results from the methods highlight the importance of feature selection for gender prediction from handwriting.

A new courtesy amount recognition module of a Check Reading System

Abstract: A new courtesy amount recognition module of CENPARMIpsilas check reading system (CRS) is proposed in this paper. The module consists of 3 main segments: pre-processing, segmentation and recognition, and post-processing. A new feedback-based segmentation algorithm is adopted for the segmentation task. Besides one individual numeral recognizer for numerals from dasia0psila to dasia9psila, one convolutional neural network(CNN) recognizer for ldquo00rdquo and ldquo000rdquo numeral strings is also integrated into our module for the recognition task. The experimental results on the Quebec Bell Check database show that the recognition rate of the courtesy amount has improved from 41.2% to 74.3%.

Integration of contextual information in handwriting recognition systems

Abstract: This paper investigates different strategies allowing integrationof contextual information during the feature extractionstage of a cursive handwriting HMM-based recognitionsystem. First we propose to use linear discriminant analysis(LDA) in order to integrate the class information duringfeature set building. Secondly several zoning strategies areused to integrate local contextual information. Finally, aweighting technique is proposed in association with zoningwith the aim of integrating handwriting style. Some experimentswere carried out and the results show the interest ofthe proposed strategies.

Gender Detection from Handwritten Documents Using Concept of Transfer-Learning

Abstract: Offline gender detection from Arabic handwritten documents is a very challenging task because of the high similarity between an individual’s writings and the complexity of the Arabic language as well. In this paper, we propose a new way to detect the writer gender from scanned handwritten documents that mainly based on the concept of transfer-learning. We used a pre-trained knowledge from two convolution neural networks (CNN): GoogleNet, and ResNet, then we applied it on our data-set. We use this two CNN architectures as fixed feature extractors. For the analysis and the classification stage, we used a support vector machine (SVM). The performance of the two CNN architectures concerning accuracy is 80.05% for GoogleNet, 83.32% for ResNet.

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.