From Anthrax to ZIP Codes- The Handwriting is on the Wall Venu Govindaraju Dept. of Computer Science...
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![Page 1: From Anthrax to ZIP Codes- The Handwriting is on the Wall Venu Govindaraju Dept. of Computer Science & Engineering University at Buffalo venu@cedar.buffalo.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062500/56649d5a5503460f94a3ad63/html5/thumbnails/1.jpg)
From Anthrax to ZIP Codes-The Handwriting is on the Wall
Venu GovindarajuDept. of Computer Science &
EngineeringUniversity at Buffalo
![Page 2: From Anthrax to ZIP Codes- The Handwriting is on the Wall Venu Govindaraju Dept. of Computer Science & Engineering University at Buffalo venu@cedar.buffalo.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062500/56649d5a5503460f94a3ad63/html5/thumbnails/2.jpg)
Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
![Page 3: From Anthrax to ZIP Codes- The Handwriting is on the Wall Venu Govindaraju Dept. of Computer Science & Engineering University at Buffalo venu@cedar.buffalo.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062500/56649d5a5503460f94a3ad63/html5/thumbnails/3.jpg)
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
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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
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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
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RCR Overview
Bar Code Sorter
RemoteEncodin
g
Advanced Facer
CancelerMulti-Line
OCR
Image
RCR
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At the Right Price
Processing Type Cost/1000 Pieces
Manual $47.78
Mechanized $27.46
Automated $5.30
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80% encode rate and counting!
Handwriting Encode Rate
0%
10%20%
30%40%
50%
60%70%
80%
Date
En
co
de
Ra
te
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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
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Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
![Page 11: From Anthrax to ZIP Codes- The Handwriting is on the Wall Venu Govindaraju Dept. of Computer Science & Engineering University at Buffalo venu@cedar.buffalo.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062500/56649d5a5503460f94a3ad63/html5/thumbnails/11.jpg)
Role Handwriting Recognition in Address Interpretation
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• <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
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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
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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
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Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
![Page 16: From Anthrax to ZIP Codes- The Handwriting is on the Wall Venu Govindaraju Dept. of Computer Science & Engineering University at Buffalo venu@cedar.buffalo.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062500/56649d5a5503460f94a3ad63/html5/thumbnails/16.jpg)
Handwriting Recognition
Context Ranked Lexicon
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Multiple Choice Question
ContextRanked Lexicon
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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
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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
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Holistic FeaturesSlant Norm
Turn Points
Position Grid and gaps
Ascender
Descender
Reference Lines
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Lexicon Reduction and Verification
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Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
![Page 23: From Anthrax to ZIP Codes- The Handwriting is on the Wall Venu Govindaraju Dept. of Computer Science & Engineering University at Buffalo venu@cedar.buffalo.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062500/56649d5a5503460f94a3ad63/html5/thumbnails/23.jpg)
Grapheme Models
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Structural FeaturesBAG
JunctionLoops
LoopTurns
End
End
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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
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Continuous Attributes
grapheme
pos orientation
angle
Down cusp
3.0 -90o
Up loop
Down arc
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Stochastic Model
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Observations
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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
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Interactive Models[McClelland and Rumelhart, Psychological Review, 1981]
ABLE TRIPTRAP
A TN
Words
Letters
Features
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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
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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
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Features
4 gradient features
5 moment features
Vector code book
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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
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Active Recognition Using Quad Trees
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Experimental Results
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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)
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Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
![Page 40: From Anthrax to ZIP Codes- The Handwriting is on the Wall Venu Govindaraju Dept. of Computer Science & Engineering University at Buffalo venu@cedar.buffalo.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062500/56649d5a5503460f94a3ad63/html5/thumbnails/40.jpg)
Fast Recognition
-Reuse matched characters
-Reuse matched sub-strings
-Parallel processing
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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
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Lexicon Density[Govindaraju, Slavik, and Xue, IEEE PAMI 2002]
Lexicon 1 Lexicon 2
Me MeHe MemoSo MemoryTo MemoirsIn Mellon
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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
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Outline
Success in Postal Application Role of Handwriting
Recognition Recognition Models Interactive Cognitive Models New Research Areas Other Applications
![Page 45: From Anthrax to ZIP Codes- The Handwriting is on the Wall Venu Govindaraju Dept. of Computer Science & Engineering University at Buffalo venu@cedar.buffalo.edu.](https://reader036.fdocuments.net/reader036/viewer/2022062500/56649d5a5503460f94a3ad63/html5/thumbnails/45.jpg)
Bank Check Recognition
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PCR Trend Analysis
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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
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Medical Text Recognition and Data Mining
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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”
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Binarization
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Historic Manuscripts
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Summary Handwriting recognition technology Pattern recognition task Lexicon holds domain specific
knowledge Adaptive methods Classifier combination methods Many applications