Machine Learning in Document Analysis and Recognition

  1.  Introduction to Document Analysis and Recognition
  2. Structure Extraction in Printed Documents Using Neural Approaches
  3. Machine Learning for Reading Order Detection in Document Image Understanding
  4. Decision-Based Specification and Comparison of Table Recognition Algorithms
  5. Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction
  6. Classification and Learning Methods for Character
  7. Recognition: Advances and Remaining Problems
  8. Combining Classifiers with Informational Confidence
  9. Self-Organizing Maps for Clustering in Document Image Analysis
  10. Adaptive and Interactive Approaches to Document Analysis
  11. Cursive Character Segmentation Using Neural Network Techniques
  12. Multiple Hypotheses Document Analysis
  13. Learning Matching Score Dependencies for Classifier Combination
  14. Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition
  15. Review of Classifier Combination Methods
  16. Machine Learning for Signature Verification
  17. Off-line Writer Identification and Verification Using Gaussian Mixture Models
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