examination

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