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Advances in Pattern Recognition - ICAPR 2001

Second International Conference Rio de Janeiro, Brazil, March 11-14, 2001 Proceedings

  • Conference proceedings
  • © 2001

Overview

Editors:
  1. Sameer Singh

    1. Department of Computer Science, University of Exeter, Exeter, UK

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  2. Nabeel Murshed

    1. Computational Intelligence Group, Tuiuti University of Parana, Curitiba, Brazil

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  3. Walter Kropatsch

    1. Institute of Computer Aided Automation PRIP-Group 1832, Vienna University of Technology, Wien, Austria

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2013)

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Table of contents (46 papers)

  1. Front Matter

    Pages I-XV
    Download chapter PDF

  3. NEURAL NETWORKS & COMPUTATIONAL INTELLIGENCE

    1. A Cascaded Genetic Algorithm for efficient optimization and pattern matching

      • Gautam Garai, B. B. Chaudhuri
      Pages 32-39

    2. Using Unlabelled Data to Train a Multilayer Perceptron

      • Antanas Verikas, Adas Gelzinis, Kerstin Malmqvist, Marija Bacauskiene
      Pages 40-49

    3. A Neural Multi -Expert Classification System for MPEG Audio Segmentation

      • M. De Santo, G. Percannella, C. Sansone, M. Vento
      Pages 50-59

    4. Pattern Recognition with Quantum Neural Networks

      • A.A. Ezhov
      Pages 60-71

    5. Pattern Matching and Neural Networks based Hybrid Forecasting System

      • Sameer Singh, Jonathan Fieldsend
      Pages 72-82

    6. Invariant Face Detection in Color Images Using Orthogonal Fourier-Mellin Moments and Support Vector Machines

      • Terrillon Jean-Christophe, Mahdad N. Shirazi, Daniel McReynolds, Mohamed Sadek, Yunlong Sheng, Shigeru Akamatsu et al.
      Pages 83-92

  4. CHARACTER RECOGNITION & DOCUMENT ANALYSIS

    1. Character Extraction from Interfering Background - Analysis of Double-sided Handwritten Archival Documents

      • Chew Lim Tan, Ruini Cao, Qian Wang, Peiyi Shen
      Pages 93-102

    2. An Enhanced HMM Topology in an LBA Framework for the Recognition of Handwritten Numeral Strings

      • Alceu de S. Britto Jr., Robert Sabourin, Flavio Bortolozzi, Ching Y. Suen
      Pages 105-114

    3. Segmentation of Printed Arabic Text

      • Adnan Amin
      Pages 115-126

    4. A Time—Length Constrained Level Building Algorithm for Large Vocabulary Handwritten Word Recognition

      • Alessandro L. Koerich, Robert Sabourin, Ching Y. Suen
      Pages 127-136

    5. Preventing Overfitting in Learning Text Patterns for Document Categorization

      • Markus Junker, Andreas Dengel
      Pages 137-146

    6. Image Document Categorization using Hidden Tree Markov Models and Structured Representations

      • Michelangelo Diligenti, Paolo Frasconi, Marco Gori
      Pages 147-156

    7. Handwriting Quality Evaluation

      • Victor Kulesh, Kevin Schaffer, Ishwar Sethi, Mark Schwartz
      Pages 157-165

  5. FEATURE SELECTION & ANALYSIS

    1. Texture Based Look-Ahead for Decision-Tree Induction

      • Ming Dong, Ravi Kothari
      Pages 166-175

    2. Feature Based Decision Fusion

      • Nayer M. Wanas, Mohamed S. Kamel
      Pages 176-185

    3. Feature Selection Based on Fuzzy Distances Between Clusters: First Results on Simulated Data

      • Teófilo E. Campos, Isabelle Bloch, Roberto M. Cesar Jr.
      Pages 186-195
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Keywords

About this book

The paper is organized as follows: In section 2, we describe the no- orientation-discontinuity interfering model based on a Gaussian stochastic model in analyzing the properties of the interfering strokes. In section 3, we describe the improved canny edge detector with an ed- orientation constraint to detect the edges and recover the weak ones of the foreground words and characters; In section 4, we illustrate, discuss and evaluate the experimental results of the proposed method, demonstrating that our algorithm significantly improves the segmentation quality; Section 5 concludes this paper. 2. The norm-orientation-discontinuity interfering stroke model Figure 2 shows three typical samples of original image segments from the original documents and their magnitude of the detected edges respectively. The magnitude of the gradient is converted into the gray level value. The darker the edge is, the larger is the gradient magnitude. It is obvious that the topmost strong edges correspond to foreground edges. It should be noted that, while usually, the foreground writing appears darker than the background image, as shown in sample image Figure 2(a), there are cases where the foreground and background have similar intensities as shown in Figure 2(b), or worst still, the background is more prominent than the foreground as in Figure 2(c). So using only the intensity value is not enough to differentiate the foreground from the background. (a) (b) (c) (d) (e) (f)

Editors and Affiliations

  • Department of Computer Science, University of Exeter, Exeter, UK

    Sameer Singh

  • Computational Intelligence Group, Tuiuti University of Parana, Curitiba, Brazil

    Nabeel Murshed

  • Institute of Computer Aided Automation PRIP-Group 1832, Vienna University of Technology, Wien, Austria

    Walter Kropatsch

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