# An Artificial Neural Network Approach for User Class-Dependent Off-Line Sentence Segmentation

Save this PDF as:

Size: px
Start display at page:

Download "An Artificial Neural Network Approach for User Class-Dependent Off-Line Sentence Segmentation"

## Transcription

2 in this paper classifies a set of features as a word or a space between words. One difficulty that emerges when using ANNs with images is how to achieve a representative set of features to be inserted as the input of the classifier. We decided to use nine geometrical quantities, based on Marti and Bunke s paper [7], calculated over a sliding window of one column width and the height of the image. These characteristics are acquired from left to right on each handwritten text line column. The input image is then represented by a sequence of feature vectors with 9 dimensions versus image width (Figure 1). Fig. 1. Sliding Window Architecture The nine features extracted from each window are explained as follows: 1) Window weight: total number of black pixels. m f 1 = p(x, y), where p is the pixel value (0 or 1) and m is the image height 2) Center of gravity. f 2 = 1 m y p(x, y) m 3) Second order moment. f 3 = 1 m m 2 y 2 p(x, y) 4) Position of the upper contour: coordinate of the highest window pixel. 5) Position of the lower contour: coordinate of the lowest window pixel. 6) Gradient of the upper contour: direction (up, straight or down) acquired comparing the position of the upper contour of the previous column and current column. 7) Gradient of the lower contour: direction (up, straight or down) acquired comparing the position of the lower contour of the previous column and current column. 8) Black-white transitions: total number of black-white transitions observed in up to down direction. 9) Black pixels between the upper and lower contours. The input of the system is represented by the handwritten text line images. We have not used any kind of normalization (such as skew, slant or writing width). Therefore, some of the nine features presented in [7] were modified in an attempt to equalize the influence of each feature over the classification. Basically, the modification was the addition of a normalization factor: Features 1, 4, 5 and 9: the normalization factor is 1/(image height); Feature 2 and 3: the normalization factor is the 1/(maximum value that each formula can reach). This value occurs when all pixels of the image column are black. Only two classes are needed for the segmentation problem designed in this paper. Class 0 represents the intra-word columns and class 1 the inter-word column. A. System Overview This section details the system phases. 1) Pattern composition: A flowchart representing the first system phase is illustrated in Figure 2. Initially, the system receives images from the text lines as input and executes Feature Extraction. Each image column is then represented by nine features. The next step is to generate the expected classification for each column ( Column Classification ). The columns in which the coordinates belong to a word are classified as Class 0. Otherwise, columns are classified as Class 1. Column classification can be performed automatically, because we have used handwritten text line images from the IAM Database 3.0 [8]. This database has metainformation on the lines that describes the Bounding Boxes of words in the handwritten text lines. Pattern Generation consists of joining the nine features to their respective classification in order to create a pattern for each column. Fig. 2. Pattern composition It is difficult to classify a pattern as belonging to a word or a gap without analyzing its neighbors. Thus, Pattern Grouping was developed to improve the ANN classification performance. A pattern is originally composed of nine features and one class identifier. After the grouping process for N patterns, a pattern will have: N 9 features and one class identifier. The created pattern classification is the same as the original inner pattern. Table I displays a size-three pattern grouping (N =3). In the first line, there are seven patterns with their feature set and respective class. F i is the representation of an image column by its 9 features. After the grouping process, International Joint Conference on Neural Networks (IJCNN 2008)

3 five patterns are created (line two) with the feature set composed of the features of the three original patterns. The classification of the new pattern corresponds to the original inner pattern. TABLE I SIZE-THREE PATTERN GROUPING Original pattern F1 F2 F3 F4 F5 F6 F7 Pattern class Created Pattern F1F2F3 F2F3F4 F3F4F5 F4F5F6 F5F6F7 Pattern class The Grouped Pattern Repository (Figure 2), stores all patterns that will be used in the ANN training and test phases. 2) ANN Training and Test: The second phase of the system is illustrated by the flowchart in Figure 3. Fig. 3. ANN Training and Test In this stage, three pattern sets are retrieved from the repository (Request data sets): training, validation and test sets. The patterns created from a single image must be grouped and ordered into a unique data set. The ANN is trained with the two former pattern sets (Train ANN), and then an evaluation is performed, classifying the patterns of the test set (Test ANN). Two kinds of errors are calculated in the test set classification: i) The Pattern classification error refers to the percentage of wrongly classified patterns; ii) The Segmentation classification error considers the number of wrongly classified runs. A run is a pattern sequence that has the same classification (belonging to the same class). The pattern classification error has no relevance to our work, as the segmentation error considers a sequence of patterns rather than an isolated pattern. Thus, one or more wrongly classified patterns in a single word is considered an single segmentation error. Considering the pattern classification of the supposed text line in Figure 4, it is possible to exemplify how the Segmentation classification error is calculated. There are five runs, three words (Class 0) and two gaps between words (Class 1). The ANN Classification of the pattern failed in two runs - the first and third. Thus, the segmentation error is 2/5 or 40%. In Figure 4, no margin of error in the word boundary was considered. However, if we adopt one pixel as error tolerance, then a single wrongly classified pattern localized in the word boundary is not considered as belonging to the word and, consequently, the segmentation error rate is not increased. Fig. 4. Segmentation error III. EXPERIMENTS AND RESULTS Like Marti and Bunke [3] and Manmatha and Rothfeder [4], our experiments were performed using the IAM-database [8]. This database contains forms with handwritten English texts from different writers, which can be used to train and test text recognition, writer identification, text segmentation, etc. All forms, text lines, words and sentences extracted are available for downloading. A XML with the metainformation of the text lines is also available. The XML information contains the description of all words in the text line. The coordinates of all the text components are also described. The ANN segmentation method described in this paper was evaluated using all the handwritten text line from the writers of a subset denoted by C03 in the IAM database. We have ignored handwritten lines with XML information that indicates a segmentation error. Thus, 489 image lines were used to build the data sets for training and testing. We have used the handwritten text line of each writer separately for training and testing (user-dependent evaluation). Two handwritten text lines were used for training, another two for validation and the remaining lines were used for testing. In this kind of experiment, one can achieve better rates for similar user writing styles. The experiments performed here considered a margin of error (explained at the end of Section II-A.2) of three pixels. Figure 5 shows an example of the distance between the margin of error and the Bounding Box of the word. Fig. 5. The dotted line represents the margin of error adopted by the automatic evaluation procedure and the rectangle represents the XML bounding box. In our experiments, two parameters were empirically defined to achieve the best segmentation error rate using the ANN method presented in this paper: Number of neurons in the hidden layer: in the range tested [5, 50], the number of neurons in the hidden layer that produced the best performance was 30. Input size: the amount of patterns ( Pattern Grouping size) used as input for the ANN that produced the best result was 40. The range tested was [5, 50]. A. Post-processing In order to improve the segmentation performance, we have developed a post-processing technique, which consists 2008 International Joint Conference on Neural Networks (IJCNN 2008) 2725

4 of using a sliding window over the sequence of classified patterns to change the pattern classification. If the patterns located in the window neighborhood have the same classification, then we change the window patterns to the same class as the neighbor patterns. Otherwise, no changes are performed. The size of the window must be empirically defined. Figure 6 illustrated the over-segmentation and undersegmentation error rates using our post-processing technique. The horizontal axis represents the sliding window size and the vertical axis represents the error rate produced by oversegmentation and under-segmentation. Note that the undersegmentation error rate increases with the window enlargement. This occurs because the post-processing technique forces a larger sequence of patterns to be classified as a unique word or space between words. The opposite behavior is observed in the over-segmentation error rate. According to Figure 6, the system can be adjusted to increase the over-segmentation error rather than the undersegmentation error, or vice-versa. This can be useful for adjusting the system to different styles of writing. Using a Size 4 Window, the Equal Error Rate is achieved (oversegmentation and under-segmentation error rate 4%). The error rates in Figure 6 were achieved from the mean of error of all handwritten text lines tested. Fig. 6. Over and under-segmentation error rate. B. System evaluation For a better evaluation of the ANN segmentation method, the Convex Hull segmentation method described in [3] was developed. The accuracy of both methods was evaluated using the same data set and the same error margin of three pixels was considered. Table II displays the error rate achieved by the different methods. Convex Hull technique with the best configuration. ANN without post-processing (Window 0). ANN with best post-processing performance. The was achieved with the Size 9 Window (Window 9). The ANN error rates were obtained from the average of 10 runs. TABLE II ERROR RATES OF CONVEX HULL AND ANN BASED METHOD WITH AND WITHOUT POST-PROCESSING. Window 0 Window 9 ID CH Over Under Total Over Under Total , x Figure 7 presents six box-plots of the post-processing accuracy. Nearly all the box-plots suggest that an optimum post-processing window size can be obtained for each writer. For example, a Size 9 Window is the best choice for postprocessing for User 154, achieving 96.45% accuracy. The same behavior did not occur in the User 151 box-plot, as the standard deviations for this writer s error rate were the largest. IV. CONCLUSIONS The present paper addressed the problem of sentence segmentation. Our approach seeks to overcome inherent difficulties in the Gap Metrics approach, such as the heuristics needed to optimize and adapt the system to different applications in handwritten sentence segmentation; and the vocabulary limitation in other segmentation methods. We presented an ANN-Based approach for off-line handwritten sentence segmentation. Assessments were performed under writer-dependent conditions on a sub-set from the IAM Database. Our experiments demonstrated that the ANN-based approach achieved better results for more writers in comparison to the Convex Hull segmentation method. No heuristics were used to adapt or improve system performance. Our method is learning-based and is therefore more appropriate for use in segmentation tasks. In future work, the proposed method should be tested under writer-independent conditions. REFERENCES [1] G. Seni and E. Cohen, External word segmentation of off-line handwritten text lines, Pattern Recognition, vol. 27, pp , [2] U. Mahadevan and R. C. Nagabushnam, Gap metrics for word separation in handwritten lines, Third International Conference on Document Analysis and Recognition, vol.1, pp , [3] U.V. Marti and H. Bunke, Text Line Segmentation and Word Recognition in a System for General Writer Independent Handwriting Recognition, Proc. Sixth Intl Conf. Document Analysis and Recognition, pp , [4] Manmatha, R., Rothfeder, J.L., A Scale Space Approach for Automatically Segmenting Words from Historical Handwritten Documents, IEEE Transactions on Pattern Analysis And Machine Intelligence, vol. 27, pp , [5] M. Morita, R. Sabourin, F. Bortolozzi and C. Y. Suen, Segmentation and recognition of handwritten dates: an HMM-MLP hybrid approach, International Journal on Document Analysis and Recognition, pp , International Joint Conference on Neural Networks (IJCNN 2008)

5 Fig. 7. Box-Plot of the Post-Processing Accuracy Rates [6] F. Lthy, T. Varga and H. Bunke, Using Hidden Markov Models as a Tool for Handwritten Text Line Segmentation, Ninth International Conference on Document Analysis and Recognition, vol.1, pp. 8-12, [7] U.V. Marti and H. Bunke. Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. Int. Journal of Pattern Recognition and Artificial Intelligence, 15(1): [8] IAM Handwriting Database 3.0. Available in: { fki/iamdb/} 2008 International Joint Conference on Neural Networks (IJCNN 2008) 2727

### Word Segmentation of Off-line Handwritten Documents

Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department

### Segmentation and Recognition of Handwritten Dates

Segmentation and Recognition of Handwritten Dates y M. Morita 1;2, R. Sabourin 1 3, F. Bortolozzi 3, and C. Y. Suen 2 1 Ecole de Technologie Supérieure - Montreal, Canada 2 Centre for Pattern Recognition

### Transcript Mapping for Historic Handwritten Document Images

Transcript Mapping for Historic Handwritten Document Images Catalin I Tomai, Bin Zhang and Venu Govindaraju CEDAR UB Commons, 520 Lee Entrance, Suite 202, Amherst,NY,14228-2567 E-mail: catalin,binzhang,govind

### LINE AND WORD SEGMENTATION OF HANDWRITTEN TEXT DOCUMENTS WRITTEN IN GURMUKHI SCRIPT USING MID POINT DETECTION TECHNIQUE

LINE AND WORD SEGMENTATION OF HANDWRITTEN TEXT DOCUMENTS WRITTEN IN GURMUKHI SCRIPT USING MID POINT DETECTION TECHNIQUE Payal Jindal 1, Dr. Balkrishan Jindal 2 1 Research Scholar, YCOE, Talwandi Sabo(India)

### Fusion of Multiple Handwritten Word Recognition Techniques

Fusion of Multiple Handwritten Word Recognition Techniques B. Verma' and P. Gader2 'School of Information Technology Griffith University-Gold Coast PMB 50, Gold Coast Mail Centre QLD 9726, Australia E-mail:

### OCR for Arabic using SIFT Descriptors With Online Failure Prediction

OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,

### Gender Classification Based on FeedForward Backpropagation Neural Network

Gender Classification Based on FeedForward Backpropagation Neural Network S. Mostafa Rahimi Azghadi 1, M. Reza Bonyadi 1 and Hamed Shahhosseini 2 1 Department of Electrical and Computer Engineering, Shahid

### Handwritten Word Recognition Using MLP based Classifier: A Holistic Approach

www.ijcsi.org 422 Handwritten Word Recognition Using MLP based Classifier: A Holistic Approach Ankush Acharyya 1, Sandip Rakshit 2, Ram Sarkar 3, Subhadip Basu 3, Mita Nasipuri 3 1 Department of Information

### Classification with Deep Belief Networks. HussamHebbo Jae Won Kim

Classification with Deep Belief Networks HussamHebbo Jae Won Kim Table of Contents Introduction... 3 Neural Networks... 3 Perceptron... 3 Backpropagation... 4 Deep Belief Networks (RBM, Sigmoid Belief

### QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents

### arxiv: v3 [cs.lg] 9 Mar 2014

Learning Factored Representations in a Deep Mixture of Experts arxiv:1312.4314v3 [cs.lg] 9 Mar 2014 David Eigen 1,2 Marc Aurelio Ranzato 1 Ilya Sutskever 1 1 Google, Inc. 2 Dept. of Computer Science, Courant

### Lecture 1: Machine Learning Basics

1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

### Intelligent Tutoring Systems using Reinforcement Learning to teach Autistic Students

Intelligent Tutoring Systems using Reinforcement Learning to teach Autistic Students B. H. Sreenivasa Sarma 1 and B. Ravindran 2 Department of Computer Science and Engineering, Indian Institute of Technology

### Realtime Online Daily Living Activity Recognition Using Head-Mounted Display

Realtime Online Daily Living Activity Recognition Using Head-Mounted Display https://doi.org/10.3991/ijim.v11i3.6469 Fais Al Huda Brawijaya University, Malang, Indonesia fais.developer@gmail.com Herman

### L12: Template matching

Introduction to ASR Pattern matching Dynamic time warping Refinements to DTW L12: Template matching This lecture is based on [Holmes, 2001, ch. 8] Introduction to Speech Processing Ricardo Gutierrez-Osuna

### Active Selection of Training Examples for Meta-Learning

Active Selection of Training Examples for Meta-Learning Ricardo B. C. Prudêncio Department of Information Science Federal University of Pernambuco Av. dos Reitores, s/n - CEP 50670-901 - Recife (PE) -

### Advanced Probabilistic Binary Decision Tree Using SVM for large class problem

Advanced Probabilistic Binary Decision Tree Using for large class problem Anita Meshram 1 Roopam Gupta 2 and Sanjeev Sharma 3 1 School of Information Technology, UTD, RGPV, Bhopal, M.P., India. 2 Information

### Discriminative Learning of Feature Functions of Generative Type in Speech Translation

Discriminative Learning of Feature Functions of Generative Type in Speech Translation Xiaodong He Microsoft Research, One Microsoft Way, Redmond, WA 98052 USA Li Deng Microsoft Research, One Microsoft

### Discriminative Learning of Feature Functions of Generative Type in Speech Translation

Discriminative Learning of Feature Functions of Generative Type in Speech Translation Xiaodong He Microsoft Research, One Microsoft Way, Redmond, WA 98052 USA Li Deng Microsoft Research, One Microsoft

### Novel Segmentation Method for Optical Braille Character Identification

Novel Segmentation Method for Optical Braille Character Identification W.D. Chamalee N. De Silva, N.G. Chandana Srilal, Harsha B. Athapaththu and L. Ranathunga Department of Information Technology, University

### Adaptive Behavior with Fixed Weights in RNN: An Overview

& Adaptive Behavior with Fixed Weights in RNN: An Overview Danil V. Prokhorov, Lee A. Feldkamp and Ivan Yu. Tyukin Ford Research Laboratory, Dearborn, MI 48121, U.S.A. Saint-Petersburg State Electrotechical

### Kobe University Repository : Kernel

Title Author(s) Kobe University Repository : Kernel A Multitask Learning Model for Online Pattern Recognition Ozawa, Seiichi / Roy, Asim / Roussinov, Dmitri Citation IEEE Transactions on Neural Neworks,

### Introduction to Pattern Recognition

Introduction to Pattern Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Fall 2017 CS 551, Fall 2017 c 2017, Selim Aksoy (Bilkent University)

### Artificial Neural Networks in Data Mining

IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 6, Ver. III (Nov.-Dec. 2016), PP 55-59 www.iosrjournals.org Artificial Neural Networks in Data Mining

### Pavel Král and Václav Matoušek University of West Bohemia in Plzeň (Pilsen), Czech Republic pkral

EVALUATION OF AUTOMATIC SPEAKER RECOGNITION APPROACHES Pavel Král and Václav Matoušek University of West Bohemia in Plzeň (Pilsen), Czech Republic pkral matousek@kiv.zcu.cz Abstract: This paper deals with

### Large vocabulary off-line handwriting recognition: A survey

Pattern Anal Applic (2003) 6: 97 121 DOI 10.1007/s10044-002-0169-3 ORIGINAL ARTICLE A. L. Koerich, R. Sabourin, C. Y. Suen Large vocabulary off-line handwriting recognition: A survey Received: 24/09/01

### On-line recognition of handwritten characters

Chapter 8 On-line recognition of handwritten characters Vuokko Vuori, Matti Aksela, Ramūnas Girdziušas, Jorma Laaksonen, Erkki Oja 105 106 On-line recognition of handwritten characters 8.1 Introduction

### NoiseOut: A Simple Way to Prune Neural Networks

NoiseOut: A Simple Way to Prune Neural Networks Mohammad Babaeizadeh, Paris Smaragdis & Roy H. Campbell Department of Computer Science University of Illinois at Urbana-Champaign {mb2,paris,rhc}@illinois.edu.edu

### Deep Neural Networks for Acoustic Modelling. Bajibabu Bollepalli Hieu Nguyen Rakshith Shetty Pieter Smit (Mentor)

Deep Neural Networks for Acoustic Modelling Bajibabu Bollepalli Hieu Nguyen Rakshith Shetty Pieter Smit (Mentor) Introduction Automatic speech recognition Speech signal Feature Extraction Acoustic Modelling

### INPE São José dos Campos

INPE-5479 PRE/1778 MONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND COVER CLASSIFICATION IN A NEDRAL NETWORK ENVIRONNENT Maria Suelena S. Barros Valter Rodrigues INPE São José dos Campos 1993 SECRETARIA

### Combining Text Classifiers and Hidden Markov Models for Information Extraction

International Journal on Artificial Intelligence Tools c World Scientific Publishing Company Combining Text Classifiers and Hidden Markov Models for Information Extraction Flavia A. Barros Center of Informatics,

### IMBALANCED data sets (IDS) correspond to domains

Diversity Analysis on Imbalanced Data Sets by Using Ensemble Models Shuo Wang and Xin Yao Abstract Many real-world applications have problems when learning from imbalanced data sets, such as medical diagnosis,

### Lecture 6: Course Project Introduction and Deep Learning Preliminaries

CS 224S / LINGUIST 285 Spoken Language Processing Andrew Maas Stanford University Spring 2017 Lecture 6: Course Project Introduction and Deep Learning Preliminaries Outline for Today Course projects What

Intelligent tools in business to business training A. Drigas, S. Kouremenos, J. Vrettaros, D. Kouremenos & L. Koukianakis NCSR Demokritos - Department of technological applications Ag. Paraskevi, 15310,

### Word Sense Disambiguation using case based Approach with Minimal Features Set

Word Sense Disambiguation using case based Approach with Minimal Features Set Tamilselvi P * Research Scholar, Sathyabama Universtiy, Chennai, TN, India Tamil_n_selvi@yahoo.co.in S.K.Srivatsa St.Joseph

### Learning Methods for Fuzzy Systems

Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8

### Introduction of connectionist models

Introduction of connectionist models Introduction to ANNs Markus Dambek Uni Bremen 20. Dezember 2010 Markus Dambek (Uni Bremen) Introduction of connectionist models 20. Dezember 2010 1 / 66 1 Introduction

### TANGO Native Anti-Fraud Features

TANGO Native Anti-Fraud Features Tango embeds an anti-fraud service that has been successfully implemented by several large French banks for many years. This service can be provided as an independent Tango

### Modelling Student Knowledge as a Latent Variable in Intelligent Tutoring Systems: A Comparison of Multiple Approaches

Modelling Student Knowledge as a Latent Variable in Intelligent Tutoring Systems: A Comparison of Multiple Approaches Qandeel Tariq, Alex Kolchinski, Richard Davis December 6, 206 Introduction This paper

### ARABIC ONLINE HANDWRITING RECOGNITION USING NEURAL NETWORK

ARABIC ONLINE HANDWRITING RECOGNITION USING NEURAL NETWORK Abdelkarim Mars 1 and Georges Antoniadis 2 1 Laboratory LIDILEM, Alpes University, Grenoble, French 2 Laboratory LIDILEM, Alpes University, Grenoble,

### Artificial Neural Networks

Artificial Neural Networks Outline Introduction to Neural Network Introduction to Artificial Neural Network Properties of Artificial Neural Network Applications of Artificial Neural Network Demo Neural

### Zaki B. Nossair and Stephen A. Zahorian Department of Electrical and Computer Engineering Old Dominion University Norfolk, VA, 23529

SMOOTHED TIME/FREQUENCY FEATURES FOR VOWEL CLASSIFICATION Zaki B. Nossair and Stephen A. Zahorian Department of Electrical and Computer Engineering Old Dominion University Norfolk, VA, 23529 ABSTRACT A

### Automatic Crime Report Classification through a Weightless Neural Network

Automatic Crime Report Classification through a Weightless Neural Network Rafael Adnet Pinho, Walkir A. T. Brito, Claudia L. R. Motta and Priscila Vieira Lima Federal University of Rio de Janeiro (UFRJ)

### Dudon Wai Georgia Institute of Technology CS 7641: Machine Learning Atlanta, GA

Adult Income and Letter Recognition - Supervised Learning Report An objective look at classifier performance for predicting adult income and Letter Recognition Dudon Wai Georgia Institute of Technology

### Synthesizer control parameters. Output layer. Hidden layer. Input layer. Time index. Allophone duration. Cycles Trained

Allophone Synthesis Using A Neural Network G. C. Cawley and P. D.Noakes Department of Electronic Systems Engineering, University of Essex Wivenhoe Park, Colchester C04 3SQ, UK email ludo@uk.ac.essex.ese

### Support Vector Machines for Handwritten Numerical String Recognition

Support Vector Machines for Handwritten Numerical String Recognition Luiz S. Oliveira and Robert Sabourin Pontifícia Universidade Católica do Paraná, Curitiba, Brazil Ecole de Technologie Supérieure -

### Software Requirements Specification Version 1.1

Software Requirements Specification Version 1.1 Project Title: Team ID: Project Guide: College: Team Members: Offline Writer Recognition using Neural Networks EGURU10698 M.V. Raghnunadh National Institute

### Performance Comparison of RBF networks and MLPs for Classification

Performance Comparison of RBF networks and MLPs for Classification HYONTAI SUG Division of Computer and Information Engineering Dongseo University Busan, 617-716 REPUBLIC OF KOREA hyontai@yahoo.com http://kowon.dongseo.ac.kr/~sht

### Pattern Classification and Clustering Spring 2006

Pattern Classification and Clustering Time: Spring 2006 Room: Instructor: Yingen Xiong Office: 621 McBryde Office Hours: Phone: 231-4212 Email: yxiong@cs.vt.edu URL: http://www.cs.vt.edu/~yxiong/pcc/ Detailed

### DRAFT VERSION. Adaptive Instructional Planning using Ontologies

Karampiperis P. and Sampson D. (00). Adaptive Instructional Planning using Ontologies. In Proc. of the th IEEE International Conference on Advanced Learning Technologies ICALT 00, Joensuu, Finland. Adaptive

### Machine Learning and Applications in Finance

Machine Learning and Applications in Finance Christian Hesse 1,2,* 1 Autobahn Equity Europe, Global Markets Equity, Deutsche Bank AG, London, UK christian-a.hesse@db.com 2 Department of Computer Science,

### An intelligent Q&A system based on the LDA topic model for the teaching of Database Principles

World Transactions on Engineering and Technology Education Vol.12, No.1, 2014 2014 WIETE An intelligent Q&A system based on the LDA topic model for the teaching of Database Principles Lin Cui & Caiyin

### Arabic Script Web Document Language Identifications Using Neural Network

Arabic Script Web Document Language Identifications Using Neural Network Ali Selamat, Ng Choon Ching, Siti Nurkhadijah Aishah Ibrahim 1 ) Abstract This paper presents experiments in identifying language

### M3 - Machine Learning for Computer Vision

M3 - Machine Learning for Computer Vision Traffic Sign Detection and Recognition Adrià Ciurana Guim Perarnau Pau Riba Index Correctly crop dataset Bootstrap Dataset generation Extract features Normalization

### Outliers Elimination for Error Correction Algorithm Improvement

Outliers Elimination for Error Correction Algorithm Improvement Janusz Kolbusz and Pawel Rozycki University of Information Technology and Management in Rzeszow jkolbusz@wsiz.rzeszow.pl,prozycki@wsiz.rzeszow.pl

### Adaptation of Mamdani Fuzzy Inference System Using Neuro - Genetic Approach for Tactical Air Combat Decision Support System

Adaptation of Mamdani Fuzzy Inference System Using Neuro - Genetic Approach for Tactical Air Combat Decision Support System Cong Tran 1, Lakhmi Jain 1, Ajith Abraham 2 1 School of Electrical and Information

### Reinforcement Learning with Deep Architectures

000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050

### AUTOMATIC TRAINING DATA SYNTHESIS FOR HANDWRITING RECOGNITION USING THE STRUCTURAL CROSSING-OVER TECHNIQUE

AUTOMATIC TRAINING DATA SYNTHESIS FOR HANDWRITING RECOGNITION USING THE STRUCTURAL CROSSING-OVER TECHNIQUE Sirisak Visessenee 1, *, Sanparith Marukatat 2, and Rachada Kongkachandra 3 1,3 Department of

### GRADUAL INFORMATION MAXIMIZATION IN INFORMATION ENHANCEMENT TO EXTRACT IMPORTANT INPUT NEURONS

Proceedings of the IASTED International Conference Artificial Intelligence and Applications (AIA 214) February 17-19, 214 Innsbruck, Austria GRADUAL INFORMATION MAXIMIZATION IN INFORMATION ENHANCEMENT

### Feedback Prediction for Blogs

Feedback Prediction for Blogs Krisztian Buza Budapest University of Technology and Economics Department of Computer Science and Information Theory buza@cs.bme.hu Abstract. The last decade lead to an unbelievable

### Programming Assignment2: Neural Networks

Programming Assignment2: Neural Networks Problem :. In this homework assignment, your task is to implement one of the common machine learning algorithms: Neural Networks. You will train and test a neural

### The Truth is in There - Rule Extraction from Opaque Models Using Genetic Programming

The Truth is in There - Rule Extraction from Opaque Models Using Genetic Programming Ulf Johansson Rikard König Lars Niklasson Department of Business and Informatics Department of Business and Informatics

### Refine Decision Boundaries of a Statistical Ensemble by Active Learning

Refine Decision Boundaries of a Statistical Ensemble by Active Learning a b * Dingsheng Luo and Ke Chen a National Laboratory on Machine Perception and Center for Information Science, Peking University,

### Transactions on Information and Communications Technologies vol WIT Press, ISSN

Using Data Mining to Learn the Patterns of Pitch Variation in Chinese Speech Tingshao Zhu&Wen Gao Institute of Computing Technology, Academia Sinica Beijing, 18. RR. China Abstract Pitch model is very

### A SELF-LEARNING NEURAL NETWORK

769 A SELF-LEARNING NEURAL NETWORK A. Hartstein and R. H. Koch IBM - Thomas J. Watson Research Center Yorktown Heights, New York ABSTRACf We propose a new neural network structure that is compatible with

### Word Discrimination Based on Bigram Co-occurrences

Word Discrimination Based on Bigram Co-occurrences Adnan El-Nasan, Sriharsha Veeramachaneni, George Nagy DocLab, Rensselaer Polytechnic Institute, Troy, NY 1218 elnasan@rpi.edu, veeras@rpi.edu, nagy@ecse.rpi.edu

### Lexicon-Driven Word Recognition Based on Levenshtein Distance

, pp.11-20 http://dx.doi.org/10.14257/ijseia.2014.8.2.02 Lexicon-Driven Word Recognition Based on Levenshtein Distance Perdana Adhitama, Soo Hyung Kim and In Seop Na * School of Electronics and Computer

### Task Decomposition Based on Class Relations: A Modular Neural Network Architecture for Pattern Classification

Task Decomposition Based on Class Relations: A Modular Neural Network Architecture for Pattern Classification Bao-Liang Lu and Masami Ito Bio-Mimetic Control Research Center, the Institute of Physical

### Towards a constructive multilayer perceptron for regression task using non-parametric clustering. A case study of Photo-Z redshift reconstruction

Towards a constructive multilayer perceptron for regression task using non-parametric clustering. A case study of Photo-Z redshift reconstruction C. Arouri E. Mephu Nguifo S. Aridhi C. Roucelle G. Bonnet-Loosli

### Explorations in vector space the continuous-bag-of-words model from word2vec. Jesper Segeblad

Explorations in vector space the continuous-bag-of-words model from word2vec Jesper Segeblad January 2016 Contents 1 Introduction 2 1.1 Purpose........................................... 2 2 The continuous

### Performance Analysis of Various Data Mining Techniques on Banknote Authentication

International Journal of Engineering Science Invention ISSN (Online): 2319 6734, ISSN (Print): 2319 6726 Volume 5 Issue 2 February 2016 PP.62-71 Performance Analysis of Various Data Mining Techniques on

### Rituparna Sarkar, Kevin Skadron and Scott T. Acton

A META-ALGORITHM FOR CLASSIFICATION BY FEATURE NOMINATION Rituparna Sarkar, Kevin Skadron and Scott T. Acton Electrical and Computer Engineering, University of Virginia Computer Science Department, University

### Arabic Braille Recognition and Transcription into Text and Voice

Arabic Braille Recognition and Transcription into Text and Voice Saad D. Al-Shamma and Sami Fathi Abstract This paper presents a system for a design and implementation of Optical Arabic Braille Recognition(OBR)

### (-: (-: SMILES :-) :-)

(-: (-: SMILES :-) :-) A Multi-purpose Learning System Vicent Estruch, Cèsar Ferri, José Hernández-Orallo, M.José Ramírez-Quintana {vestruch, cferri, jorallo, mramirez}@dsic.upv.es Dep. de Sistemes Informàtics

### Speech Emotion Recognition Using Support Vector Machine

Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,

### Using Big Data Classification and Mining for the Decision-making 2.0 Process

Proceedings of the International Conference on Big Data Cloud and Applications, May 25-26, 2015 Using Big Data Classification and Mining for the Decision-making 2.0 Process Rhizlane Seltani 1,2 sel.rhizlane@gmail.com

### DEEP STACKING NETWORKS FOR INFORMATION RETRIEVAL. Li Deng, Xiaodong He, and Jianfeng Gao.

DEEP STACKING NETWORKS FOR INFORMATION RETRIEVAL Li Deng, Xiaodong He, and Jianfeng Gao {deng,xiaohe,jfgao}@microsoft.com Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA ABSTRACT Deep stacking

### HANDWRITTEN DIGIT RECOGNITION FOR MANAGING EXAMINATION SCORE IN PAPER-BASED TEST

HANDRITTEN DIGIT RECOGNITION FOR MANAGING EXAMINATION SCORE IN PAPER-BASED TEST,* APICHAT SURATANEE, NANTAPORN LERTSARI, SETHAAT KAMPHASEE, KRITSADA SRIKET Department of Mathematics, Faculty of Applied

### Cooperative Interactive Cultural Algorithms Based on Dynamic Knowledge Alliance

Cooperative Interactive Cultural Algorithms Based on Dynamic Knowledge Alliance Yi-nan Guo 1, Shuguo Zhang 1, Jian Cheng 1,2, and Yong Lin 1 1 College of Information and Electronic Engineering, China University

### On Multiclass Universum Learning

On Multiclass Universum Learning Sauptik Dhar Naveen Ramakrishnan Vladimir Cherkassky Mohak Shah Robert Bosch Research and Technology Center, CA University of Minnesota, MN University of Illinois at Chicago,

### Constraint Satisfaction Adaptive Neural Network and Heuristics Combined Approaches for Generalized Job-Shop Scheduling

474 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 11, NO. 2, MARCH 2000 Constraint Satisfaction Adaptive Neural Network and Heuristics Combined Approaches for Generalized Job-Shop Scheduling Shengxiang Yang

### Inventor Chung T. Nguyen NOTTCE. The above identified patent application is available for licensing. Requests for information should be addressed to:

Serial No. 802.572 Filing Date 3 February 1997 Inventor Chung T. Nguyen NOTTCE The above identified patent application is available for licensing. Requests for information should be addressed to: OFFICE

### Explaining similarity in CBR

Explaining similarity in CBR Eva Armengol, Santiago Ontañón and Enric Plaza Artificial Intelligence Research Institute (IIIA-CSIC) Campus UAB, 08193 Bellaterra, Catalonia (Spain) Email: {eva, enric}@iiia.csic.es

### Improving the Effectiveness of Learning Content Retrieval through Content Classification and Profiling

Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 8, August 2014,

### Improving Machine Learning Through Oracle Learning

Brigham Young University BYU ScholarsArchive All Theses and Dissertations 2007-03-12 Improving Machine Learning Through Oracle Learning Joshua Ephraim Menke Brigham Young University - Provo Follow this

### Artificial Neural Networks for Storm Surge Predictions in NC. DHS Summer Research Team

Artificial Neural Networks for Storm Surge Predictions in NC DHS Summer Research Team 1 Outline Introduction; Feedforward Artificial Neural Network; Design questions; Implementation; Improvements; Conclusions;

### OHASD: The First On-Line Arabic Sentence Database Handwritten on Tablet PC

OHASD: The First On-Line Arabic Sentence Database Handwritten on Tablet PC Randa I. M. Elanwar, Mohsen A. Rashwan, and Samia A. Mashali Abstract In this paper we present the first Arabic sentence dataset

### Predicting Academic Success from Student Enrolment Data using Decision Tree Technique

Predicting Academic Success from Student Enrolment Data using Decision Tree Technique M Narayana Swamy Department of Computer Applications, Presidency College Bangalore,India M. Hanumanthappa Department

### Improving Real-time Expert Control Systems through Deep Data Mining of Plant Data

Improving Real-time Expert Control Systems through Deep Data Mining of Plant Data Lynn B. Hales Michael L. Hales KnowledgeScape, Salt Lake City, Utah USA Abstract Expert control of grinding and flotation

### Improving Document Clustering by Utilizing Meta-Data*

Improving Document Clustering by Utilizing Meta-Data* Kam-Fai Wong Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong kfwong@se.cuhk.edu.hk Nam-Kiu Chan Centre

### FILTER BANK FEATURE EXTRACTION FOR GAUSSIAN MIXTURE MODEL SPEAKER RECOGNITION

FILTER BANK FEATURE EXTRACTION FOR GAUSSIAN MIXTURE MODEL SPEAKER RECOGNITION James H. Nealand, Alan B. Bradley, & Margaret Lech School of Electrical and Computer Systems Engineering, RMIT University,

### Simulated Annealing Neural Network for Software Failure Prediction

International Journal of Softare Engineering and Its Applications Simulated Annealing Neural Netork for Softare Failure Prediction Mohamed Benaddy and Mohamed Wakrim Ibnou Zohr University, Faculty of Sciences-EMMS,

### Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition Paul Hensch 21.01.2014 Seminar aus maschinellem Lernen 1 Large-Vocabulary Speech Recognition Complications 21.01.2014

### TRACK AND FIELD PERFORMANCE OF BP NEURAL NETWORK PREDICTION MODEL APPLIED RESEARCH - LONG JUMP AS AN EXAMPLE

TRACK AND FIELD PERFORMANCE OF BP NEURAL NETWORK PREDICTION MODEL APPLIED RESEARCH - LONG JUMP AS AN EXAMPLE YONGKUI ZHANG Tianjin University of Sport, 300381, Tianjin, China E-mail: sunflower2001@163.com

### On The Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis

On The Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis Asriyanti Indah Pratiwi, Adiwijaya Telkom University, Telekomunikasi Street No 1, Bandung 40257, Indonesia

### Speech Recognition at ICSI: Broadcast News and beyond

Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI

### Evolving Artificial Neural Networks

Evolving Artificial Neural Networks Christof Teuscher Swiss Federal Institute of Technology Lausanne (EPFL) Logic Systems Laboratory (LSL) http://lslwww.epfl.ch christof@teuscher.ch http://www.teuscher.ch/christof

### Incremental Retrieval of documents relevant to a topic

Incremental Retrieval of documents relevant to a topic Caroline Lyon, Bob Dickerson, James Malcolm University of Hertfordshire, UK c.m.lyon@herts.ac.uk Introduction As new participants to TREC, on the

### OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,

### Report on the Third Contest on Symbol Recognition

Report on the Third Contest on Symbol Recognition Ernest Valveny 1, Philippe Dosch 2, Alicia Fornes 1 and Sergio Escalera 1 1 Computer Vision Center, Dep. Ciències de la Computació Universitat Autònoma