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Bangla Grammar pattern recognition Using Shift Reduce Parser 5

Bangla Grammar Pattern Recognition Using Shift Reduce Parser

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5th international conference on Informatics, Electronics and Vision (ICIEV)

作者： Rabbi, Rafsan Zani Shuvo, Mohammad Insanur Rahman Hasan, K. M. Azharul Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna Bangladesh

ISBN: (纸本)9781509012695

Parser plays a very important role in computational linguistics. In this paper, here we describe a parsing technique for Bangla grammar recognition. the parser is, by nature, a shift reduce parser and constructs a parse table based on LR strategy. It takes the Context Free Grammar (CFG) of the Bangla language as input and constructs parser table from the grammar. the parse table is visited on bottom-up approach. this parser is free from the problem of the left factoring and left recursion. To avoid the inflection (BIVOKTI) of Bangla we describe a new approach. Hence only the main form of the Bangla word is stored in the repository. Our experiment shows that the scheme can detect all forms of Bangla sentences even for nontraditional forms.

关键词： Parser Context free grammar Computational linguistics Bottom up parsing Canonical forms Parse table

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Script line separation from Indian multi-script documents 5

Script line separation from Indian multi-script documents

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5th international conference on Document Analysis and recognition, ICDAR 1999

作者： Pal, U. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Calcutta35 India

ISBN: (纸本)0769503187

In a multi-lingual country like India, a document page may contain more than one script form. Under the three-language formula, the document may be printed in English, Devnagari and one of the other official Indian languages. For OCR of such a document page, it is necessary to separate these three script forms before feeding them to the OCRs of individual scripts. In this paper, an automatic technique of separating the text lines using script characteristics and shape based features is presented. At present, the system has an overall accuracy of about 98.5%. © 1999 IEEE.

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Web Application System of Handwritten Text recognition 5

Web Application System of Handwritten Text Recognition

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5th international conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Bodnia, Yevhen Kozulia, Mariia Natl Tech Univ Kharkiv Polytech Inst Kirpichova Str 2 Kharkiv Ukraine

the problem of text recognition is becoming increasingly important due to the active introduction of digital computing and the widespread use of word processors. pattern recognition is one of the most difficult from a mathematical point of view and one of the most popular areas of artificial intelligence programming. In the work is researched approaches and methods of solving text recognition problem, improved the performance of the available algorithms for text recognition and created algorithmic software. According to the analysis, neural networks were selected for handwriting recognition. the main advantage of using neural networks is a good generalization ability, the ability to use context analysis and recognize a symbol based on the surrounding symbols. the software implementation features of Hopfield and convolutional neural network, genetic algorithm, which were chosen as effective methods for recognizing handwritten text, were considered. Algorithmic software and web application that uses these methods for the task of handwritten text recognition is developed.

关键词： Handwritten text recognition pattern recognition recognition methods neural network genetic algorithm convolutional neural network Hopfield neural network machine learning data models

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Application of neural network pattern recognition to the geometrical characterisation of optical fibres

Application of neural network pattern recognition to the geo...

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Proceedings of the 5th international conference on Image Processing and its Applications

作者： Hadland, S.M. Nelson, J.C.C. Univ of Leeds Leeds United Kingdom

Automation of the image shearing measurement technique applied to optical fibre geometry is discussed. the use of both edge detection filters and a multi-layer perceptron neural network to identify the critical 'just touch' condition are compared. the repeatability of cladding diameter measurements using both methods are presented which demonstrate the superiority of the neural network technique.

关键词： Neural networks

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Motion-based recognition of people in EigenGait space

Motion-based recognition of people in EigenGait space

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5th IEEE international conference on Automatic Face and Gesture recognition

作者： BenAbdelkader, C Cutler, R Davis, L Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)0769516025

A motion-based, correspondence-free technique for human gait recognition in monocular video is presented. We contend that the planar dynamics of a walking person are encoded in a 2D plot consisting of the pairwise image similarities of the sequence of images of the person, and that gait recognition can be achieved via standard pattern classification of these plots. We use background modelling to track the person for a number of frames and extract a sequence of segmented images of the person. the self-similarity plot is computed via correlation of each pair of images in this sequence, For recognition, the method applies Principal Component Analysis to reduce the dimensionality of the plots, then uses the k-nearest neighbor rule in this reduced space to classify an unknown person. this method is robust to tracking and segmentation errors, and to variation in clothing and background. It is also invariant to small changes in camera viewpoint and walking speed. the method is tested on outdoor sequences of 44 people with 4 sequences of each taken on two different days, and achieves a classification rate of 77%. It is also tested on indoor sequences of 7 people walking on a treadmill, taken from 8 different viewpoints and on 7 different days. A classification rate of 78% is obtained for near-fronto-parallel views, and 65% on average over all view.

关键词： pattern recognition

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Empowering Knowledge Based Speech Understanding through Statistics 5

Empowering Knowledge Based Speech Understanding through Stat...

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5th international conference on Spoken Language Processing, ICSLP 1998

作者： Fischer, J. Haas, J. Nöth, E. Niemann, H. Deinzer, F. University of Erlangen-Nuremberg Pattern Recognition Martensstraße 3 Erlangen91058 Germany

In this paper we present an innovative approach to speech understanding which is based on a fine-grained knowledge representation automatically compiled from a semantic network and on iterative optimization. Besides allowing an efficient exploitation of parallelism, any-time capability is provided since after each iteration step a (sub-)optimal solution is always available. We apply this approach to a real-world task, which is a dialog system able to answer queries about the German train timetable. In order to speed up the search for the best interpretation of an utterance we make use of statistical methods, e.g. neural networks, n-grams, and classification trees, which are trained on application relevant utterances collected over the public telephone network. At the moment the real-time factor for interpreting the initial user's utterance is 0.7. © 1998. 5th international conference on Spoken Language Processing, ICSLP 1998. All rights reserved.

关键词： Knowledge representation

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An Improved Particle Swarm Optimization Algorithm For Speaker recognition

An Improved Particle Swarm Optimization Algorithm For Speake...

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IEEE 5th international conference on Advanced Computational Intelligence (ICACI)

作者： Luo, Ruiling Cai, Wenqing Chen, Min Zhu, Dongqin Shihezi Univ Coll Informat Sci & Technol Shihezi 832000 Xinjiang Peoples R China

ISBN: (纸本)9781467317443

Considering the Particle Swam Optimization (PSO) is easily relapsing into local extremum, an improved PSO(IPSO) is proposed in this paper. In the new algorithm, we apply the evolution speed factor as the trigger conditions to stochastically disturb the local optimal solution. the IPSO algorithm can not only improve extraordinarily the convergence velocity in the evolutionary optimization, but also can adjust the balance between global and local exploration suitably. then a speaker recognition approach using this improved algorithm to train Support vector machine (SVM) is presented. the experimental results show that the SVM optimized by IPSO achieves higher classification accuracy than the standard SVM and effectively improves the speaker identification speed and accuracy.

关键词： convergence evolutionary computation particle swarm optimisation pattern classification speaker recognition support vector machines

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Overview of application of artificial neural network to partial discharge pattern classification

Overview of application of artificial neural network to part...

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Proceedings of the 1997 5th IEEE international conference on Properties and Applications of Dielectric Materials. Part 1 (of 2)

作者： Cho, Kyu-Bock Oh, Joo-Young Hanseo Univ Chungam Korea Republic of

this paper presents an overview of an artificial neural network (ANN) based partial discharge (PD) distribution pattern recognition problem to power system application. After referring briefly to the developments of ANN technique-based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical application in test laboratories and on site. the structure of a PD data base and selection of learning of PD data pattern, extraction of relevant characteristic feature or information for PD recognition are discussed. Some practical problems encountered in the neuro-fuzzy techniques based real time PD recognition are also addressed.

关键词： Partial discharges

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Ideal chaotic pattern recognition using the modified adachi neural network 5

Ideal chaotic pattern recognition using the modified adachi ...

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5th international conference on Chaotic Modeling and Simulation, CHAOS 2012

作者： Qin, Ke Oommen, B.J. University of Electronic Science and Technology of China Chengdu611731 China Carleton University OttawaONK1S 5B6 Canada University of Agder Postboks 509 Grimstad4898 Norway

the benchmark of a chaotic pattern recognition (PR) system is the following: First of all, one must be able to train the system with a set of "training" patterns. Subsequently, as long as there is no testing pattern, the system must be chaotic. However, if the system is, thereafter, presented with an unknown testing pattern, the behavior must ideally be as follows. If the testing pattern is not one of the trained patterns, the system must continue to be chaotic. As opposed to this, if the testing pattern is truly one of the trained patterns (or a noisy version of a trained pattern), the system must switch to being periodic, with the specific trained pattern appearing periodically at the output. this is truly an ambitious goal, with the requirement of switching from chaos to periodicity being the most demanding. the Adachi Neural Network (AdNN) [1-5] has properties which are pseudo-chaotic, but it also possesses limited PR characteristics. As opposed to this, the Modified Adachi Neural Network (M-AdNN) proposed by Calitoiu et al [7], is a fascinating NN which has been shown to possess the required periodicity property desirable for PR applications. In this paper, we shall tune the parameters of the M-AdNN for its weights, steepness and external inputs, to yield a new NN, which we shall refer to as the Ideal-M-AdNN. Using a rigorous Lyapunov analysis, we shall analyze the chaotic properties of the Ideal-M-AdNN, and demonstrate its chaotic characteristics. thereafter, we shall verify that the system is also truly chaotic for untrained patterns. But most importantly, we demonstrate that it is able to switch to being periodic whenever it encounters patterns with which it was trained. Apart from being quite fascinating, as far as we know, the theoretical and experimental results presented here are both unreported and novel. Indeed, we are not aware of any NN that possesses these properties. © 2012 5th Chaotic Modeling and Simulation international conference All Rights Res

关键词： Neural networks

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LBP vs SVD for Color Images Feature Extraction 5

LBP vs SVD for Color Images Feature Extraction

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5th IEEE international conference on Smart Instrumentation, Measurement and Application (ICSIMA)

作者： Faridah, Y. Nasir, Haida Nikolay, S. Kushsairy, A. K. Khan, Sheroz Univ Kuala Lumpur Malaysian Inst Informat Technol 1016 Jalan Sultan Ismail Kuala Lumpur 50250 Malaysia Int Islamic Univ Malaysia Batu 8Jalan Sg Pusu Gombak 53100 Selangor Malaysia Technion Israel Inst Technol Haifa Israel

ISBN: (纸本)9781538662885

Images are important element in our daily life. We use images to identify things or associate them with others that become important in decision making or correlate with other object for identification purpose. Feature extraction need to be applied to images for them to be useful for the mentioned purpose. By definition, feature extraction is the transformation of input data into a set of features, normally distinctive properties of input patterns that differentiate between many input of patterns. It is also the process to represent raw image (important criteria or parts of) to help in decision making such as pattern detection, classification, recognition or image processing or machine learning. the process of extracting information from images is one of the important elements in authenticating process of biometric system. Many techniques for extracting features have been emerging in the current research. LBP (Local Binary pattern) has been a popular technique in biometric systems used for feature extraction. LBP is a texture feature ex fraction method which produce clearer image result. Another favorite technique known as SVD (Single Value Decomposition) has been selected, discussed and compared with the latter. A sample of still color images were tested for both methods and the results are compared and discussed further in here. this paper concentrates on these two techniques for image processing in a biometric system.

关键词： Algorithm LBP (Local Binary pattern) SVD (Single Value Decomposition) Feature Extraction Texture

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