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

This paper discusses the success of handwritten word recognition in postal applications, including lexicon-driven and lexicon-free approaches, and explores new research areas such as lexicon density and reduction. It also highlights other applications of handwriting recognition and the impact on cost and automation in the postal industry.

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

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  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 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

  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. 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

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

  8. The Right Technology • Technological Nexus • Sophisticated Algorithms • High Speed Processors • Large Disk Capacities • High Speed Memories

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

  10. 80% encode rate and counting!

  11. 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

  12. 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

  13. Handwritten Address Interpretation (HWAI) Chaincode Generation Address Block Image Pre-scan with Digit Recognizer Line Segmentation Word Separation Parsing a) shape b) syntax Digit String Recognition Input Yes Phrase Recognition Encoding Strategy Database Queries Output Finalized? 14221 3851 11 No Adaptive Image Enhancement 5, 9, or 11 digit encode OR reject Pass 1 or Pass 2 Pass 1 Pass 2 Output

  14. 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

  15. CEDAR Delivery Point File • 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

  16. CEDAR Relevant Statistics • 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

  17. 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

  18. Handwriting Recognition Bryant 2.3 Boston 1.8 Bidwell 2.6 James 4.7 Buffalo 8.9 : : : : : Word Recognition Engine Signal BostonBuffaloWilliamsvilleBidwellJamesByrant.... Context Lexicon Ranked lexicon with distance scores

  19. 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] WMR 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

  20. CMR • 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

  21. 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

  22. Multiple Choice Paradigm • Amherst b) Buffalo c) Boston • d) None of the above

  23. Grapheme Models

  24. Stochastic Models and Continuous Attributes

  25. Results

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

  27. Cognitive Handwritten Word 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

  28. 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

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

  30. 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

  31. Active Recognition Using Quad Trees

  32. Experimental Results

  33. Results 10 class digit recognition 25656 training and 12242 test (Postal +NIST)

  34. 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

  35. 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

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

  37. 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

  38. 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

  39. Bank Check Recognition

  40. PCR Trend Analysis

  41. 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

  42. Medical Text Recognition and Data Mining

  43. 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”

  44. Binarization

  45. Historic Manuscripts

  46. Mapping Snippets with Transcribed Text

  47. Summary • Handwriting recognition technology • Pattern recognition task • Lexicon holds domain specific knowledge • Adaptive methods • Classifier combination methods • Many applications

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