from anthrax to zip codes- the handwriting is on the wall venu govindaraju dept. of computer science...
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From Anthrax to ZIP Codes-The Handwriting is on the Wall
Venu GovindarajuDept. of Computer Science &
EngineeringUniversity at Buffalo
venu@cedar.buffalo.edu
Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
USPS HWAI Background
Postal Sponsorship Started – 1984 370 Academic Articles Published Millions of Letters Examined Many Experimental Systems Built and
Tested Migrated from Hardware to Software
System Only Postal Research Continuously Funded
Items to be Recognized, Read, and Evaluated (Machine printed and Script)
Delivery address, sender´s address, endorsements Linear Codes, Mail Class Indicia (2D-Codes, Meter Marks)
Meter Mark
Sender’s Address
Delivery Address
Linear Code
Digital Post MarkEndorsem
entIn Case of Undeliverable as Addressed Return to Sender
Pattern Recognition Tasks
Deployed.. USA
250 P&DC sites 27 Remote Encoding Centers 25 Billion Images Processed Annually 89% Automated Bar-coding
UK 67 Processing Centers 27 Million Pieces Per Day, 9.7 Million Pieces Per Hour Peak
Australia
RCR Overview
Bar Code Sorter
RemoteEncodin
g
Advanced Facer
CancelerMulti-Line
OCR
Image
RCR
At the Right Price
Processing Type Cost/1000 Pieces
Manual $47.78
Mechanized $27.46
Automated $5.30
80% encode rate and counting!
Handwriting Encode Rate
0%
10%20%
30%40%
50%
60%70%
80%
Date
En
co
de
Ra
te
Impact Applications of CEDAR research helping
to automate tasks at IRS and USPS 1st year that USPS used CEDAR-developed
software to read handwritten addresses on envelopes, saved $100 million
1997-1999 USPS deployment of CEDAR-developed RCRs, USPS saved 12 million work hours and over $340 million
500 scientific publications and 10 patents
Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
Role Handwriting Recognition in Address Interpretation
• <ZIP Code, Primary Number>– Create street name lexicon
<06478, 110>• DPF yields 8 street names
• ZIP+4 yields 31 street names (on average about 5 times more)
HAWLEY RD 1034NEWGATE RD 1533BEE MOUNTAIN RD 1615DORMAN RD 1642BOWERS HILL RD 1757FREEMAN RD 1781PUNKUP RD 1784PARK RD 6124
Context Provided by Postal Directories
One record per delivery point in USA Provided weekly by USPS, San Mateo Raw DPF
138 million records 15 GB (114 bytes per record); 41,889 ZIP Code files
Fields of interest to HWAI ZIP Code, street name, primary number,
secondary number, add-on
ContextCEDAR
ZIP Code 30% of ZIP Codes contain a single street name 5% of ZIP Codes contain a single primary number 2% of ZIP Codes contain a single add-on
<ZIP Code, primary number> Maximum number of records returned is 3,071
<ZIP Code, add-on> Maximum number of records returned is 3,070
Power of Context
CEDAR
Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
Handwriting Recognition
Context Ranked Lexicon
Multiple Choice Question
ContextRanked Lexicon
Lexicon Driven Model
1 2 3 4 5 6 7 8 9
w[7.6]
w[7.2]r[3.8]
w[5.0]
w[8.6]
o[7.6]r[6.3]
d[4.9]
w[5.0]
o[6.6]
o[6.0]
o[7.2]o[10.6] d[6.5]
d[4.4]
r[7.5]r[6.4]
o[7.8]r[8.6]
o[8.7]r[7.4]
r[7.6]
o[8.3]
o[7.7]r[5.8]
1 2 3 4 5 6 7 8 9
o[6.1]
Find the best way of accounting for characters ‘w’, ‘o’, ‘r’, ‘d’ buy consuming all segments 1 to 8 in the process
Distance between lexicon entry ‘word’ first character ‘w’ and the image between:- segments 1 and 4 is 5.0- segments 1 and 3 is 7.2- segments 1 and 2 is 7.6
Lexicon Free Model
4
5
67 82 3
1
1 32 4 5 6 7 8i[.8], l[.8] u[.5], v[.2]
w[.6], m[.3]
w[.7]
i[.7]u[.3]
m[.2]m[.1]
r[.4]
d[.8]o[.5]
-Image from 1 to 3 is a in with 0.5 confidence-Image from segment 1 to 4 is a ‘w’ with 0.7 confidence-Image from segment 1 to 5 is a ‘w’ with 0.6 confidence and an ‘m’ with 0.3 confidence
Find the best path in graph from segment 1 to 8
w o r d
Holistic FeaturesSlant Norm
Turn Points
Position Grid and gaps
Ascender
Descender
Reference Lines
Lexicon Reduction and Verification
Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
Grapheme Models
Structural FeaturesBAG
JunctionLoops
LoopTurns
End
End
Feature Extraction and Ordering
Critical node: removal disconnects a connected component.
2-degree critical nodes keep feature ordering from left to right.
LeftComponent
RightComponent
Loop
EndTurns
Junction
LoopsEnd
Turns
Continuous Attributes
grapheme
pos orientation
angle
Down cusp
3.0 -90o
Up loop
Down arc
Stochastic Model
Observations
Results
Lex size
Top WMR %
SM CA%
10 1 96.86 96.56
2 98.80 98.77
100 1 91.36 89.12
2 95.30 94.06
1000 1 79.58 75.38
2 88.29 86.29
20000 1 62.43 58.14
2 71.07 66.49
Interactive Models[McClelland and Rumelhart, Psychological Review, 1981]
ABLE TRIPTRAP
A TN
Words
Letters
Features
Interactive Recognition
T-crossings, loops, ascenders, descenders, length
West Central StreetWest Main StreetSunset Avenue
West Central StreetEast Central StreetSunset Avenue
West Central StreetWest Central AvenueSunset Avenue
Lexicon 1 Lexicon 2 Lexicon 3
Interactive Model
features
image
Adaptive Character Recognition[Park and Govindaraju, IEEE CVPR 2000]
•Adaptive selection of features
•Adaptive number of features
•Adaptive resolutions
•Adaptive sequencing of features
•Adaptive termination conditions
Features
4 gradient features
5 moment features
Vector code book
Feature Space
|V| x |Nc| x |Ixy|
29 x 10 x 85 (quad tree, 4 levels)
Recognition rate and feature |V| GSC: |V| : 2512
Tradeoffs: space vs accuracy Hierarchical space with additional
resolution and features as needed
Active Recognition Using Quad Trees
Experimental Results
Results
Classifier Active Model Neural Net
KNN
Top 1% 95.7 % 96.4% 95.7%
Templates 612 976 3,777
Msec/char 1.45 11.5 384
Training hrs 1 24 1
25656 training and 12242 test (Postal +NIST)
Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
Fast Recognition
-Reuse matched characters
-Reuse matched sub-strings
-Parallel processing
Combination and Dynamic Selection[Govindaraju and Ianakiev, MCS 2000]
WR 1
WR 2
WR 3+Lexicon
1
Top 5
<55Top 50
image
•Optimization problem
•Combinatorial explosion in
•arrangement of recognizers
•lexicon reduction levels
Lexicon Density[Govindaraju, Slavik, and Xue, IEEE PAMI 2002]
Lexicon 1 Lexicon 2
Me MeHe MemoSo MemoryTo MemoirsIn Mellon
Classifier Performance Prediction[Xue and Govindaraju, IEEE PAMI 2002]
q: probability that recognizer make a unit distance errors
D: average distance between any two words in the lexicons
n: lexicon size; p: performance; a, k,: model parameters
ln (-ln p) = (ln q) D + a ln ln n + ln k
Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
Bank Check Recognition
PCR Trend Analysis
NYS EMS PCR FormNYS PCR Example
Thousands are filed a day.Passed from EMS to Hospital.
PCR Purpose:– Medical care/diagnosis– Legal Documentation– Quality Assurance
EMS AbbreviationsCOPD Chronic Obstructive Pulmonary DiseaseCHF Congestive Heart FailureD/S Dextrose in SalinePID Pelvic Inflammatory DiseaseGSW Gunshot WoundNKA No known allergiesKVO Keep vein openNaCL Sodium Chloride
Medical Text Recognition and Data Mining
Reading Census Forms
Lexicon Anomalies
Space: “sales man” and “salesman”
Morphology: “acct manager” and “account management”
Abbreviation
Plural: “school” and “schools”
Typographical: “managar” and “manager”
Binarization
Historic Manuscripts
Summary Handwriting recognition technology Pattern recognition task Lexicon holds domain specific
knowledge Adaptive methods Classifier combination methods Many applications
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