Download - Multiple Agents for Pattern Recognition
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Multiple Agentsfor
Pattern Recognition
Louis Vuurpijl
http://hwr.nici.kun.nl/~vuurpijl
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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
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Sources of variation(Schomaker, Plamondon et al ’00)
Affine transforms
Style Variations
Neuro-biomechanichal
VariationsOrder
Variability
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The more writers, the more….
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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........
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Pattern Recognition (I)
Raw data
X(t),Y(t),P(t)
Class labels
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Pattern Recognition (I)
Preprocessing
Segmentation
Feature Extraction
Classification
Class labels
Raw data
X(t),Y(t),P(t)
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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
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The basis of handwriting
X(t),Y(t),P(t)
Va(t)
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UNIPEN data
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Zooming in on writing styles
UNIPEN styles: print, cursive, mixed
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The lean recognition machine
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Clustering on stroke-features (IWFHR’96)
Specialization boosts recognition performance,while reducing computational and memory requirements
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Finding structure in diversity (ICDAR’97)
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Hierarchy in character shapes
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Allograph prototypes
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dScript: a MAPR system (’00)
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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)
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Pattern Recognition (II)
Raw data
Class labels
Classifier 0Classifier 0
Classifier 0Classifier 0
Classifier 0Classifier 0
Classifier 0Classifier 0
Classifier 0Classifier(i)
ClassifierCombination
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Classifier combination (van Erp’00,’02)
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Changing contexts....
- static architecture
- what if “Go 551” was intended?
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Dynamic PR
Through extra heuristic information
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Dynamic PRThrough extra features (add PENUP)
Features determinehow you look at data
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Dynamic PRKnowing when to use which feature
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Dynamic PRKnowing when to use which feature
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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
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Pattern Recognition (III)
Raw data
Class labels
MAPR
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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 PR1 and 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 cases in the past.
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Agent framework
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MAPR: Our goal
• A distributed intelligent agent framework,
• with PR modules, symbolic equivalents and PR language.
• Driven by problem constraints
• and with learning capabilities.
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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
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A proof of conceptTrained recognition system hclus
– 15557 digits from UNIPEN– 7778 train, 7779 test (95.9%)
1-7 30 5-8 14
7-2 27 1-8 12
5-3 25 0-9 12
1-2 23 8-0 11
1-2 23 8-0 11
4-1 22 4-8 10
4-6 14
1-7, 7-2, 4-6conflictssolved 97%
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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)
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Current research on MAPR
• Knowledge base
• PR language & implementation
• PR negotiation mechanisms
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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 ?