From A nthrax to Z IP Codes - The Handwriting is on the Wall

From Anthrax to ZIP Codes-The Handwriting is on the Wall

Venu GovindarajuDept. of Computer Science & Engineering

University at Buffalo

Venu@cedar.buffalo.edu

Outline

• Success in Postal Application

• Role of Handwritten Word Recognition

• Word Recognition– Lexicon Driven Word Recognition

– Lexicon Free Word Recognition

• New Models– Interactive Cognitive Models

• New Research Areas– Lexicon density

– Lexicon Reduction and Combination

• 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

Scope - Others

• Royal Mail– 67 Processing Centers– 27 Million Pieces Per Day– 9.7 Million Pieces Per Hour Peak

• Australia Post– Similar to Royal Mail

C o l l e c t i o nM a i l

R C R

B a rC o d eS o r t e r s

P O S T N E Tb a r c o d e R e s u l t s

R e m o t eE n c o d i n gS i t e

M L O C R

H a n d w r i t t e n

F I M ( p r e - b a r c o d e d )

M a c h i n e

M a i l

A F C SA F C SA F C S

P r e -B a r c o d e d

M L O C RM L O C R

U S P S M a i l F l o wM L O C RR e j e c t s

D e l i v e r yP o i n t B a r - c o d e dM a i l p i e c e s

M L O C RA s s i g n e d

I P S S

RCR Overview

Bar Code Sorter

RemoteEncodin

g

Advanced Facer

CancelerMulti-Line

OCR

Image

RCR

The Right Technology

• Technological Nexus

– Sophisticated Algorithms

– High Speed Processors

– Large Disk Capacities

– High Speed Memories

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 Handwritten Word Recognition

• Word Recognition– Lexicon Driven Word Recognition

– Lexicon Free Word Recognition

• New Models– Interactive Cognitive Models

• New Research Areas– Lexicon density

– Lexicon Reduction and Combination

• Other Applications

Chaincode Generation

Pre-scan with Digit Recognizer

Line Segmentation

Word Separation

Parsinga) shapeb) syntax

Digit String Recognition

Database Queries

Phrase Recognition

Encoding Strategy

5, 9, or 11 digit encode

OR reject

Address Block Image

Yes

Finalized?

Pass 1or

Pass 2

Adaptive Image

Enhancement

No

Pass 1 Pass 2

14221 3851 11

Input

Output

Output

Handwritten Address Interpretation (HWAI)

• <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, record type (eg., street, firm, PO

Box ..), street name, primary number, secondary number, add-on

Delivery Point FileCEDAR

• 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

Relevant Statistics

CEDAR

Outline

• Success in Postal Application

• Role of Handwritten Word Recognition

• Word Recognition– Lexicon Driven Word Recognition

– Lexicon Free Word Recognition

• New Models– Interactive Cognitive Models

• New Research Areas– Lexicon density

– Lexicon Reduction and Combination

• Other Applications

Word Recognition Engine

Bryant 2.3Boston 1.8Bidwell 2.6James 4.7Buffalo 8.9:::::

Rankedlexiconwith distance scores

Signal

Handwriting Recognition

BostonBuffaloWilliamsvilleBidwellJamesByrant....

ContextLexicon

WMR

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

CMR

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

Outline

• Success in postal application

• Role of Handwritten Word Recognition

• Word Recognition– Lexicon Driven Word Recognition

– Lexicon Free Word Recognition

• New Models– Interactive Cognitive Models

• New Research Areas– Lexicon density

– Lexicon Reduction and Combination

• Other Applications

Multiple Choice Paradigm

a) Amherst b) Buffalo c) Bostond) None of the above

Grapheme Models

Stochastic Models and Continuous Attributes

grapheme pos orientation angle

Down cusp

3.0 -90o

Up loop

Down arc

ResultsLex 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

50 98.00 98.40

20000 1 62.43 58.14

2 71.07 66.49

100 93.59 93.39

Interactive Models[McClelland and Rumelhart, Psychological Review, 1981]

ABLE TRIPTRAP

A TN

Words

Letters

Features

Cognitive Handwritten Word 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

Features4 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

ResultsClassifier Active Model Neural

NetKNN

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

10 class digit recognition

25656 training and 12242 test (Postal +NIST)

Outline

• Success in Postal Application

• Role of Handwritten Word Recognition

• Word Recognition– Lexicon Driven Word Recognition

– Lexicon Free Word Recognition

• New Models– Interactive Cognitive Models

• New Research Areas– Lexicon Reduction and Combination

– Lexicon Density and Prediction of Performance

• Other Applications

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 Handwritten Word Recognition

• Word Recognition– Lexicon Driven Word Recognition

– Lexicon Free Word Recognition

• New Models– Interactive Cognitive Models

• New Research Areas– Lexicon density

– Lexicon Reduction and Combination

• 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

Mapping Snippets with Transcribed Text

Summary

• Handwriting recognition technology

• Pattern recognition task

• Lexicon holds domain specific knowledge

• Adaptive methods

• Classifier combination methods

• Many applications