CEDARFOX-020907.pdf

Feb 20, 20230 likes30 views

This document summarizes computational approaches to questioned handwriting examination developed by the CEDAR research group. It describes the CEDAR-FOX software system, which performs writer verification and identification by computing the probability and strength of evidence of a handwriting match. The system extracts handwriting characteristics and letter formations to compare between known and questioned documents and provides ranked results. It also has tools for document properties analysis, signature verification, and searching handwritten documents. Future work is focused on improving the statistical model and developing better line segmentation techniques.

Computational Approaches to Questioned
Handwriting Examination
Sargur (Hari) Srihari
University at Buffalo
State University of New York

Computational Forensics
• Forensic domains involving pattern matching
• Motivated by Importance of Quantitative
methods in the Forensic Sciences
1. Daubert Ruling
2. High Standards established by DNA
3. Computers
1. Low Cost
2. Advances in Artificial Intelligence/Pattern Recognition
4. Improved Statistical Methods for Evidence
E.g., Aitken and Taroni, Statistics and the Evaluation of
Evidence for Forensic Scientists, Wiley, 2004

QDE
• Bureau of Justice Statistics (2002)
– Among 50 largest publicly funded crime labs
• 57% perform QD function
• 5,231 cases requested
• 1,079 backlogged at year end
• Significantly larger case load internationally
• Handwriting is common in QD case work

CEDAR Research on Handwritten QDE
• Research on quantifying discriminatory
power of handwriting since 1999
– Testing on national database, twins data
• Feedback from QDE’s in developing
computational tools
– Workshops at ASQDE,
– JtMtg of MAFS,CAFS,
– SWAFDE
• Developing Statistical Evidence Theory

CEDARFOX software system
• Writer Verification/Identification
– Probability/Strength of Evidence Computation
• Document Properties
– Line Structure, Writer Characteristics
• Signature Verification
• Document Search
System Requirements
Pentium class processor
(P4 or higher recommended)
Windows NT, 2000 or XP
128MB of RAM
30MB available disk space

Result of Verification
Feature Comparison
Table
Strength of Evidence

How is Strength of Evidence Computed?
• Handwriting characteristics are extracted
from both K and Q and their similarities
compared to the similarities in a
representative database
• Based on a data base of 1,500 writers
providing 3 pages of writing each
• Probability distributions of similarities
modeled by Gamma and Gaussian
distributions

What Handwriting Characteristics are
Computed?
Pictorial
Attribute
Scores
Letter
Formation
Scores

Writer Identification
Ranked Document List

Document Properties
Document Line Structure

Comparing Letter Formations
User selects
Character to be displayed

Comparing Letter Pairs
“th” combination and similarity score

Word Similarities
Word Comparison
And Similarity Score

Sample Preparation: Rule Line Removal
Original Ruled Text
User Control
Removed Lines

Signature Matching
Genuine Set
Scores for
Questioned
Signatures

Search Modalities
Retrieval: Word Images Retrieval: Words (Text)
Retrieval: Word Images
Query: Text Word Query: Word Image Query: Word Image

User Manual
Available
In Help
Menu
Organized by Topics
Hierarchically

Summary
• CEDAR-FOX is a system for QDE with a
focus on handwriting
• Has automated tools for writer/signature
verification/identification
• Has tools for case-work display
• Computes strength of evidence

Future Work
• Better Statistical Model
– Current statistical model in system uses
independence assumption
– Performance is not high as with better
theoretical models, e.g., neural networks
– Plan to incorporate a compromise model e.g.,
pairwise independence

Thank You
• Further Information:
• srihari@cedar.buffalo.edu
• ycshin@cedartech.com

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CEDARFOX-020907.pdf

1. Computational Approaches to Questioned Handwriting Examination Sargur (Hari) Srihari University at Buffalo State University of New York

2. Computational Forensics • Forensic domains involving pattern matching • Motivated by Importance of Quantitative methods in the Forensic Sciences 1. Daubert Ruling 2. High Standards established by DNA 3. Computers 1. Low Cost 2. Advances in Artificial Intelligence/Pattern Recognition 4. Improved Statistical Methods for Evidence E.g., Aitken and Taroni, Statistics and the Evaluation of Evidence for Forensic Scientists, Wiley, 2004

3. QDE • Bureau of Justice Statistics (2002) – Among 50 largest publicly funded crime labs • 57% perform QD function • 5,231 cases requested • 1,079 backlogged at year end • Significantly larger case load internationally • Handwriting is common in QD case work

4. CEDAR Research on Handwritten QDE • Research on quantifying discriminatory power of handwriting since 1999 – Testing on national database, twins data • Feedback from QDE’s in developing computational tools – Workshops at ASQDE, – JtMtg of MAFS,CAFS, – SWAFDE • Developing Statistical Evidence Theory

5. CEDARFOX software system • Writer Verification/Identification – Probability/Strength of Evidence Computation • Document Properties – Line Structure, Writer Characteristics • Signature Verification • Document Search System Requirements Pentium class processor (P4 or higher recommended) Windows NT, 2000 or XP 128MB of RAM 30MB available disk space

6. Writer Verification Known Questioned

7. Result of Verification Feature Comparison Table Strength of Evidence

8. How is Strength of Evidence Computed? • Handwriting characteristics are extracted from both K and Q and their similarities compared to the similarities in a representative database • Based on a data base of 1,500 writers providing 3 pages of writing each • Probability distributions of similarities modeled by Gamma and Gaussian distributions

9. What Handwriting Characteristics are Computed? Pictorial Attribute Scores Letter Formation Scores

10. Writer Identification Ranked Document List

11. Document Properties Document Line Structure

12. Word Recognition Lexicon Selection

13. Transcript Mapping

14. Comparing Letter Formations User selects Character to be displayed

15. Comparing Letter Pairs “th” combination and similarity score

16. Word Similarities Word Comparison And Similarity Score

17. Sample Preparation: Rule Line Removal Original Ruled Text User Control Removed Lines

18. Signature Matching Genuine Set Scores for Questioned Signatures

19. Searching Documents Query Image

20. Search Modalities Retrieval: Word Images Retrieval: Words (Text) Retrieval: Word Images Query: Text Word Query: Word Image Query: Word Image

21. User Manual Available In Help Menu Organized by Topics Hierarchically

22. Summary • CEDAR-FOX is a system for QDE with a focus on handwriting • Has automated tools for writer/signature verification/identification • Has tools for case-work display • Computes strength of evidence

23. Future Work • Better Statistical Model – Current statistical model in system uses independence assumption – Performance is not high as with better theoretical models, e.g., neural networks – Plan to incorporate a compromise model e.g., pairwise independence

24. Future Work: Line Segmentation

25. Thank You • Further Information: • srihari@cedar.buffalo.edu • ycshin@cedartech.com

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CEDARFOX-020907.pdf

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