Online handwriting trajectory reconstruction from kinematic sensors using temporal convolutional network

International Journal on Document Analysis and Recognition (IJDAR) - Handwriting with digital pens is a common way to facilitate human–computer interaction through the use of online...     

Multimodal interfaces

In this paper, we present an overview of research in our laboratories on Multimodal Human Computer Interfaces. The goal for such interfaces is to free human computer interaction from the limitations and acceptance barriers due to rigid operating commands and keyboards as the only/main I/O-device. Instead we move to involve all available human communication modalities. These human modalities include Speech, Gesture and Pointing, Eye-Gaze, Lip Motion and Facial Expression, Handwriting, Face Recognition, Face Tracking, and Sound Localization.     

Stable methods for recognizing acronym-expansion pairs: from rule sets to hidden Markov models

A significant portion of currently available documents exist in the form of images, for instance, as scanned documents. Electronic documents produced by scanning and OCR software contain recognition errors. This paper uses an automatic approach to examine the selection and the effectiveness of searching techniques for possible erroneous terms for query expansion. The proposed method consists of two basic steps. In the first step, confused characters in erroneous words are located and editing operations are applied to create a collection of erroneous error-grams in the basic unit of the model. The second step uses query terms and error-grams to generate additional query terms, identify appropriate matching terms, and determine the degree of relevance of retrieved document images to the user's query, based on a vector space IR model. The proposed approach has been trained on 979 document images to construct about 2,822 error-grams and tested on 100 scanned Web pages, 200 advertisements and manuals, and 700 degraded images. The performance of our method is evaluated experimentally by determining retrieval effectiveness with respect to recall and precision. The results obtained show its effectiveness and indicate an improvement over standard methods such as vectorial systems without expanded query and 3-gram overlapping. Youssef Fataicha received his B.Sc. degree from Université de Rennes1, Rennes, France, in 1982. In 1984 he obtained his M.Sc. in computer science from Université de Rennes1, France. Between 1984 and 1986 he was a lecturer at the Université de Rennes1, France. He then served as engineer, from 1987 to 2000, at {Office de l'eau potable et de l'électricité} in Morocco. Since 2001 has been a Ph.D. student at the {école de Technologie Supérieure de l'Université du Québec} in Montreal, Québec, Canada. His research interests include pattern recognition, information retrieval, and image analysis. Mohamed Cheriet received his B.Eng. in computer science from {Université des Sciences et de Technologie d'Alger} (Bab Ezouar, Algiers) in 1984 and his M.Sc. and Ph.D., also in computer science, from the University of Pierre et Marie Curie (Paris VI) in 1985 and 1988, respectively. Dr. Cheriet was appointed assistant professor in 1992, associate professor in 1995, and full professor in 1998 in the Department of Automation Engineering, {école de Technologie Supérieure} of the University of Québec, Montreal. Currently he is the director of LIVIA, the Laboratory for Imagery, Vision and Artificial Intelligence at ETS, and an active member of CENPARMI, the Centre for Pattern Recognition and Machine Intelligence. Professor Cheriet's research focuses on mathematical modeling for signal and image processing (scale-space, PDEs, and variational methods), pattern recognition, character recognition, text processing, document analysis and recognition, and perception. He has published more than 100 technical papers in these fields. He was the co-chair of the 11th and the 13th Vision Interface Conferences held respectively in Vancouver in 1998 and in Montreal in 2000. He was also the general co-chair of the 8th International Workshop on Frontiers on Handwriting Recognition held in Niagara-on-the-Lake in 2002. He has served as associate editor of the International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI) since 2000. Dr. Cheriet is a senior member of IEEE. Jian Yun Nie is a professor in the computer science department (DIRO), Université de Montreal, Québec, Canada. His research focuses on problems related to information retrieval, including multilingual and multimedia information retrieval, as well as natural language processing. Ching Y. Suen received his M.Sc. (Eng.) from the University of Hong Kong and Ph.D. from the University of British Columbia, Canada. In 1972 he joined the Department of Computer Science of Concordia University, where he became professor in 1979 and served as chairman from 1980 to 1984 and as associate dean for research of the Faculty of Engineering and Computer Science from 1993 to 1997. He has guided/hosted 65 visiting scientists and professors and supervised 60 doctoral and master's graduates. Currently he holds the distinguished Concordia Research Chair in Artificial Intelligence and Pattern Recognition and is the Director of CENPARMI, the Centre for Pattern Recognition and Machine Intelligence.Professor Suen is the author/editor of 11 books and more than 400 papers on subjects ranging from computer vision and handwriting recognition to expert systems and computational linguistics. A Google search on “Ching Y. Suen” will show some of his publications. He is the founder of the International Journal of Computer Processing of Oriental Languages and served as its first editor-in-chief for 10 years. Presently he is an associate editor of several journals related to pattern recognition.A fellow of the IEEE, IAPR, and the Academy of Sciences of the Royal Society of Canada, he has served several professional societies as president, vice-president, or governor. He is also the founder and chair of several conference series including ICDAR, IWFHR, and VI. He has been the general chair of numerous international conferences, including the International Conference on Computer Processing of Chinese and Oriental Languages in August 1988 held in Toronto, International Conference on Document Analysis and Recognition held in Montreal in August 1995, and the International Conference on Pattern Recognition held in Québec City in August 2002.Dr. Suen has given 150 seminars at major computer companies and various government and academic institutions around the world. He has been the principal investigator of 25 industrial/government research contracts and is a grant holder and recipient of prestigious awards, including the ITAC/NSERC award from the Information Technology Association of Canada and the Natural Sciences and Engineering Research Council of Canada in 1992 and the Concordia “Research Fellow” award in 1998.     

一种有效的手写体汉字组合特征的抽取与识别算法

基于特征融合的思想,从有利于模式分类的角度,推广了典型相关分析的理论,建立了广义的典型相关分析用于图像识别的理论框架。在该框架下,首先利用广义的典型相关判据准则函数,求取两组特征矢量的广义投影矢量集,构成一对变换矩阵;然后根据所提出的新的特征融合策略,对两种手写体汉字特征进行融合,所抽取的模式的相关特征矩阵,在普通分类器下取得了良好的分类效果,优于已有的特征融合方法及基于单一特征的PCA 方法和FLDA 方法。     

基于依存分析的事件识别

事件抽取是信息抽取的重要组成部分,事件识别是事件抽取的基础,事件识别的效果直接影响了事件抽取的结果.基于机器学习的方法识别事件需要从词汇中发掘更多的特征.针对当前事件识别方法中存在的不足,提出了一种基于依存分析的事件识别方法.用依存分析发掘触发词与其它词之间的句法关系,以此为特征在SVM分类器上对事件进行分类,最终实现事件识别.实验表明,基于依存分析的事件识别优于传统的事件识别方法,而融合多特征的事件识别F值可提高到69.3%.     

A survey of six International Conferences on Pattern Recognition

In order to visualize the trends in pattern recognition during the past 10 years, the full papers contained in the Proceedings of the six International Conferences on Pattern Recognition held so far were categorized and indexed. In total, 40 index terms were used.The paper supplies information about participation by various countries. The continuous shift from pattern recognition towards image processing is substantiated. The general trends in emphasis are visualized in more detail on maps as obtained by the application of correspondence analysis. Some national differences are also indicated.     

Efficient dynamic time warping for 3D handwriting recognition using gyroscope equipped smartphones

Handwriting character recognition from three-dimensional (3D) accelerometer data has emerged as a popular technique for natural human computer interaction. In this paper, we propose a 3D gyroscope-based handwriting recognition system that uses stepwise lower-bounded dynamic time warping, instead of conventional 3D accelerometer data. The results of experiments conducted indicate that our proposed method is more effective and efficient than conventional methods for user-independent recognition of the 26 lowercase letters in the English alphabet.     

Boosting modern and historical handwritten text recognition with deformable convolutions

International Journal on Document Analysis and Recognition (IJDAR) - Handwritten Text Recognition (HTR) in free-layout pages is a challenging image understanding task that can provide a relevant...     

A benchmark for unconstrained online handwritten Uyghur word recognition

International Journal on Document Analysis and Recognition (IJDAR) - Despite some interesting results from different research groups, a public database for Uyghur online handwriting recognition and...     

Intention recognition in manufacturing applications

In this article, we present a novel approach to intention recognition, based on the recognition and representation of state information in a cooperative human–robot environment. States are represented by a combination of spatial relations along with cardinal direction information. The output of the Intention Recognition Algorithms will allow a robot to help a human perform a perceived operation or, minimally, not cause an unsafe situation to occur. We compare the results of the Intention Recognition Algorithms to those of an experiment involving human subjects attempting to recognize the same intentions in a manufacturing kitting domain. In almost every case, results show that the Intention Recognition Algorithms performed as well, if not better, than a human performing the same activity.     

A survey on off-line Cursive Word Recognition

This paper presents a survey on off-line Cursive Word Recognition. The approaches to the problem are described in detail. Each step of the process leading from raw data to the final result is analyzed. This survey is divided into two parts, the first one dealing with the general aspects of Cursive Word Recognition, the second one focusing on the applications presented in the literature.     

Editorial for special issue on “Advanced Topics in Document Analysis and Recognition”

International Journal on Document Analysis and Recognition (IJDAR) -     

基于人工免疫的监督学习模型及其应用

基于免疫识别机理提出一种监督学习模型并应用于人脸识别上。将训练样本和测试样本都看成是抗原，在此基础上，将训练样本（第一类抗原）用于训练抗体集合，产生记忆抗体集合，而测试样本（第二类抗原）则用于测试该模型及算法在人脸识别上的识别效果。这一系列的过程是在利用主元分析法（PCA）对每幅图像进行预处理的基础上完成的。从实验结果可以看出，提出的监督学习模型在数据学习方面的有效性。     

Deep learning for graphics recognition: document understanding and beyond

International Journal on Document Analysis and Recognition (IJDAR) -     

Correction to: Radical-based extract and recognition networks for Oracle character recognition

International Journal on Document Analysis and Recognition (IJDAR) -     

Retraction Note: Offline scripting-free author identification based on speeded-up robust features

International Journal on Document Analysis and Recognition (IJDAR) -     

Editorial for special issue on “advanced topics in document analysis and recognition”

International Journal on Document Analysis and Recognition (IJDAR) -