1 / 36

Multiple Agents for Pattern Recognition

Multiple Agents for Pattern Recognition. Louis Vuurpijl http://hwr.nici.kun.nl/~vuurpijl. Contents. The problem: handwriting recognition PR1: The traditional solution (some) solutions at the NICI PR2: Multiple classifiers (some) solutions at the NICI PR3: Could MAPR be a solution?

dalila
Download Presentation

Multiple Agents for Pattern Recognition

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multiple AgentsforPattern Recognition Louis Vuurpijl http://hwr.nici.kun.nl/~vuurpijl

  2. Contents • The problem: handwriting recognition • PR1: The traditional solution • (some) solutions at the NICI • PR2: Multiple classifiers • (some) solutions at the NICI • PR3: Could MAPR be a solution? • our current achievements

  3. Sources of variation(Schomaker, Plamondon et al ’00) Affine transforms Style Variations Neuro-biomechanichalVariations Order Variability

  4. The more writers, the more….

  5. Is this a problem? • Shrihari ICDAR’01: “No problem” Handwriting is individual, so can be used in court (as a fingerprint)! • IWFHRxx, ICDAR, IJDAR... “Yes!” We have 99+ digit recognition, 98+ character recognition and 90+ for isolated words........

  6. Pattern Recognition (I) Raw data X(t),Y(t),P(t) Class labels

  7. Pattern Recognition (I) Preprocessing Raw data X(t),Y(t),P(t) Segmentation Feature Extraction Class labels Classification

  8. Solutions at the NICI • More than 20 years of experience in • Handwriting production (Thomassen, van Galen, Meulenbroek, Maarse, Schomaker, et al) • Handwriting recognition (Schomaker, Teulings, Vuurpijl, et al) • Keywords: • Use knowledge about human handwriting • Specialization and...... • Fusion

  9. The basis of handwriting X(t),Y(t),P(t) Va(t)

  10. UNIPEN data

  11. Zooming in on writing styles UNIPEN styles: print, cursive, mixed

  12. The lean recognition machine

  13. Clustering on stroke-features (IWFHR’96) Specialization boosts recognition performance, while reducing computational and memory requirements

  14. Finding structure in diversity (ICDAR’97)

  15. Hierarchy in character shapes

  16. Allograph prototypes

  17. dScript: a MAPR system (’00)

  18. dScript: 9 classifiers • Neural networks (MLP, Kohonen) • Nearest neighbour & clustering • Structural/geometrical • Support vector machines • Hidden markov models Fusion through classifier combination and Multiple agents (IWFHR’98,’00,’02)

  19. Pattern Recognition (II) Classifier 0 Classifier 0 Classifier 0 Classifier 0 Classifier 0 Classifier 0 Raw data Classifier 0 Classifier 0 Classifier 0 Classifier(i) Classifier Combination Class labels

  20. Classifier combination (van Erp’00,’02)

  21. Changing contexts.... • static architecture • what if “Go 551” was intended?

  22. Dynamic PR Through extra heuristic information

  23. Dynamic PR Through extra features (add PENUP) Features determine how you look at data

  24. Dynamic PR Knowing when to use which feature

  25. Dynamic PR Knowing when to use which feature

  26. Dynamic PR Knowing when to use which feature/algorithm • Through a knowledge base of PR • Through a library of PR modules • Through negotiation protocols Knowing how to use which PR module

  27. Pattern Recognition (III) Raw data MAPR Class labels

  28. What is an agent? (Wooldridge and Jennings, 1995) A software system with: • Goals:``What do you want?'' or ``What can you do?'' • I can solve 0-6 conflicts • Beliefs and reasoning:``How do you realize this goal?'' • I solve this 0-6 conflict using modules PR1and PR2 • and features #84 and #96, extracted by FE(i) and FE(j) • Assertions with confidence: • Based on my experience and these features I belief this input is a ``6'' with confidence 0.9. • I have been correct in 90% of the casesin the past.

  29. Agent framework

  30. MAPR: Our goal • A distributed intelligent agent framework, • with PR modules, symbolic equivalents and PR language. • Driven by problem constraints • and withlearning capabilities.

  31. Our current MAPR system • Distributed processing over internet using sockets. • Interfaces to KQML and Jatlite. • Agents know about the environment. • Agents know about the available PR modules and data. • Agents interact with other agents. • Agents to detect problems and conflicts

  32. A proof of concept Trained recognition system hclus • 15557 digits from UNIPEN • 7778 train, 7779 test (95.9%) 1-7, 7-2, 4-6 conflicts solved  97%

  33. But...... • This is all hard-wired if confidence top[0] too low then solve(top[0],top[1],...) solve(1,7,2) = best(1-7,1-2,7-2)

  34. Current research on MAPR • Knowledge base • PR language & implementation • PR negotiation mechanisms

  35. Conclusions • Online HWR is still unsolved • MCS can improve recognition rates, but..... • Hard-wired PR modules • Examples where dynamic PR is needed • MAPR is a new paradigm that exploits knowledge about when to use which features or algorithms But how to implement shared access to knowledge? And how to perform agent-like negotiations ?

More Related