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

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

2. Outline • Success in Postal Application • Role of Handwriting Recognition • Recognition Models • Interactive Cognitive Models • New Research Areas • Other Applications

3. 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

4. Meter Mark Digital Post Mark Sender’s Address Endorsement In Case of Undeliverable as Addressed Return to Sender Linear Code Delivery Address Pattern Recognition Tasks 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)

5. 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

6. Advanced Facer Canceler Image Multi-Line OCR RCR Remote Encoding Bar Code Sorter RCR Overview

7. At the Right Price Processing TypeCost/1000 Pieces Manual $47.78 Mechanized $27.46 Automated $5.30

8. 80% encode rate and counting!

9. 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

10. Outline • Success in Postal Application • Role of Handwriting Recognition • Recognition Models • Interactive Cognitive Models • New Research Areas • Other Applications

11. Role Handwriting Recognition in Address Interpretation

12. Context Provided by Postal Directories • <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 MOUNTAINRD 1615DORMAN RD 1642BOWERS HILL RD 1757FREEMAN RD 1781PUNKUP RD 1784PARK RD 6124

13. CEDAR Context • 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

14. CEDAR Power of Context • 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

15. Outline • Success in Postal Application • Role of Handwriting Recognition • Recognition Models • Interactive Cognitive Models • New Research Areas • Other Applications

16. Handwriting Recognition Ranked Lexicon Context

17. Multiple Choice Question Ranked Lexicon Context

18. w[5.0] o[7.7]r[5.8] r[7.6] d[4.9] o[6.1] r[6.4] w[5.0] o[6.0] r[7.5] o[8.3] o[7.6]r[6.3] w[7.6] 1 2 3 4 5 6 7 8 9 o[6.6] r[3.8] d[4.4] o[8.7]r[7.4] w[7.2] o[7.2] d[6.5] o[10.6] w[8.6] o[7.8]r[8.6] Lexicon Driven Model 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 Find the best way of accounting for characters ‘w’, ‘o’, ‘r’, ‘d’ buy consuming all segments 1 to 8 in the process

19. Lexicon Free Model • 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 w[.6], m[.3] w[.7] d[.8] o[.5] u[.5], v[.2] i[.8], l[.8] i[.7] r[.4] u[.3] m[.2] m[.1] Find the best path in graph from segment 1 to 8 w o r d

20. Holistic Features Reference Lines Slant Norm Turn Points Ascender Position Grid and gaps Descender

21. Lexicon Reduction and Verification

22. Outline • Success in Postal Application • Role of Handwriting Recognition • Recognition Models • Interactive Cognitive Models • New Research Areas • Other Applications

23. Grapheme Models

24. Structural FeaturesBAG End Loops Junction End Loop Turns

25. Feature Extraction and Ordering Critical node: removal disconnects a connected component. Loops End End Turns Junction Turns Loop 2-degree critical nodeskeep feature ordering from left to right. Right Component Left Component

26. Continuous Attributes

27. Stochastic Model

28. Observations

29. Results

30. Interactive Models [McClelland and Rumelhart, Psychological Review, 1981] ABLE TRAP TRIP Words A T N Letters Features

31. Interactive Recognition Lexicon 1 Lexicon 2 Lexicon 3 West Central StreetWest Main StreetSunset Avenue West Central StreetEast Central StreetSunset Avenue West Central StreetWest Central AvenueSunset Avenue Interactive Model features T-crossings, loops, ascenders, descenders, length image

32. 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

33. Features 4 gradient features 5 moment features Vector code book

34. 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

35. Active Recognition Using Quad Trees

36. Experimental Results

38. Results 25656 training and 12242 test (Postal +NIST)

39. Outline • Success in Postal Application • Role of Handwriting Recognition • Recognition Models • Interactive Cognitive Models • New Research Areas • Other Applications

40. Fast Recognition -Reuse matched characters -Reuse matched sub-strings -Parallel processing

41. Combination and Dynamic Selection [Govindaraju and Ianakiev, MCS 2000] image WR 1 WR 3 + 1 Top 50 Lexicon <55 WR 2 Top 5 • Optimization problem • Combinatorial explosion in • arrangement of recognizers • lexicon reduction levels

42. Lexicon Density [Govindaraju, Slavik, and Xue, IEEE PAMI 2002] Lexicon 1 Lexicon 2 Me MeHe MemoSo MemoryTo MemoirsIn Mellon

43. 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

44. Outline • Success in Postal Application • Role of Handwriting Recognition • Recognition Models • Interactive Cognitive Models • New Research Areas • Other Applications

45. Bank Check Recognition

46. PCR Trend Analysis

47. NYS EMS PCR Form NYS PCR Example Thousands are filed a day. Passed from EMS to Hospital. PCR Purpose: • Medical care/diagnosis • Legal Documentation • Quality Assurance EMS Abbreviations • COPD Chronic Obstructive Pulmonary Disease • CHF Congestive Heart Failure • D/S Dextrose in Saline • PID Pelvic Inflammatory Disease • GSW Gunshot Wound • NKA No known allergies • KVO Keep vein open • NaCL Sodium Chloride

48. Medical Text Recognition and Data Mining

49. 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”

50. Binarization