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Projects

Projects. CS 661. DAS 02, Princeton, NJ. OCR Features and Systems Degradation models, script ID, Bilingual OCR, Kannada OCR, Tamil OCR, mp versus hw checks, traffic ticket reading Handwriting Recognition Stochastic models, holistic methods, Japanese OCR Classifiers and Learning

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Projects

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  1. Projects CS 661

  2. DAS 02, Princeton, NJ • OCR Features and Systems • Degradation models, script ID, Bilingual OCR, Kannada OCR, Tamil OCR, mp versus hw checks, traffic ticket reading • Handwriting Recognition • Stochastic models, holistic methods, Japanese OCR • Classifiers and Learning • Multi-classifier systems • Layout Analysis • Skew correction, geometric methods, test/graphics separation, logical labeling • Tables and Forms • Detecting tables in HTML documents, use of graph grammars, semantics • Text Extraction • Indexing and Retrieval • Document Engineering • New Applications • CAPTCHA, Tachograph chart system, accessing driving directions

  3. ICDAR 03, Edinburgh, UK • Multiple Classifiers • Postal Automation and Check Processing • Document Understanding • HMM Classifiers • Segmentation • Character Recognition • Graphics Recognition • Non-Latin Alphabets- Kanji/Chinese, Korean/Hangul, Arabic/Indian • Web Documents, Video • Word Recognition • Image Processing • Writer Identification • Forms and Tables

  4. Project Assignments

  5. Multilingual Digital Library

  6. Control Panel Query Input Query Result Telugu and Arabic modules under development

  7. Multilingual DIA and OCR

  8. Text/Image Separation Intervals between peaks

  9. Line Separation • Ascenders & descenders interfering with lines • Region-growing approach • In Devanagari, single word is a single connected component • Grow regions using horizontally adjacent components

  10. Word Separation • In Devanagari, all characters in a word are glued together by Shirorekha • Vertical Projection profile easily separates words

  11. Multilingual OCR using HMMs

  12. Continuous Attributes

  13. Stochastic Model

  14. Observations

  15. Integrating Online and Offline Handwriting Recognition

  16. Structural FeaturesBAG End Loops Junction End Loop Turns

  17. 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

  18. Fingerprint Enhancement and Feature Extraction

  19. Fingerprint Recognition Orientation maps and minutiae detection

  20. Preprocessing Operations • Image Enhancement • Image Segmentation • Correlation among fingers Filtering

  21. Multiple Classifier Systems

  22. 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

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

  24. Interactive Handwriting Recognition

  25. Handwriting Recognition Ranked Lexicon Context

  26. Multiple Choice Question Ranked Lexicon Context

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

  28. Handwritten CAPTCHAs

  29. “CAPTCHAs”:Completely Automated Public Turing Tests to Tell Computers & Humans Apart • challenges can be generated & graded automatically (i.e. the judge is a machine) • accepts virtually all humans, quickly & easily • rejects virtually all machines • resists automatic attack for many years (even assuming that its algorithms are known?) NOTE: the machine administers, but cannot pass the test! L. von Ahn, M. Blum, N.J. Hopper, J. Langford, “CAPTCHA: Using Hard AI Problems For Security,” Proc., EuroCrypt 2003, Warsaw, Poland, May 4-8, 2003 [to appear].

  30. Yahoo!’s present CAPTCHA: “EZ-Gimpy” • Randomly pick: one English word, deformations, degradations, occlusions, colored backgrounds, etc • Better tolerated by users • Now used on a large scale to protect various services • Weaknesses: a single typeface, English lexicon

  31. Indirect Biometrics from Medical Forms Images

  32. Hard biometrics Soft biometrics Face Eye :Retina & Iris Fingerprint Hand Geometry Handwriting Speech DNA Age Ethnicity Nationality Build Gait Mannerisms Writing style (Semantic) Fields PR Statistics NLP Cog Sc Ontology Anthropometry Sociology Vision Digital Lib AI Law The Biometrics Spectrum Derived biometrics Indirect biometrics Text/News WWW Driver’s License Medical Records INS Forms • Biometric Consortium (www.biometrics.org) lists several products: • Faces (30); Fingerprints (50); Hand geometry (30); Handwriting (5); Iris (5); Multimodal (6); Retinal (2); Vein (3); Voice (22); Other (20) • NONE on soft biometrics • NONE on the fusion of indirect and derived biometrics

  33. 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

  34. Medical Text Recognition and Data Mining

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