Document Analysis and Recognition

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Document Analysis and Recognition

• CS 661

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What is a Document?

• A written or printed paper that bears the
  original, official, or legal form of something
  and can be used to furnish decisive evidence or
  information.
• Something, such as a recording or a photograph,
  that can be used to furnish evidence or
  information.
• A writing that contains information.
• Computer Science. A piece of work created with an
  application, as by a word processor.
• Computer Science. A computer file that is not an
  executable file and contains data for use by
  applications

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Document Image Analysis

• DIA is the theory and practice of recovering the
  symbol structures of digital images scanned from
  paper or produced by computer
• DIA is a subfield of Digital Image processing
• Digital images of natural objects X-rays,
  fingerprints, faces, scenery, etc. are NOT part
  of DIA
• Digital images of symbolic objects Postal
  addresses, printed articles, forms, music sheets,
  engineering drawings, topographic maps belong to
  DIA
• Source Scanners, printers, fax machines, hand!
• Incidental text license plates, billboards,
  subtitles, in photos and video
• WWW ??
• DIAs grand goal is take us to the land of
  paperless office

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Paperless Office?

• Traditional transmission and storage of
  information has been by paper documents
• Documents are increasingly originating on the
  computer
• Documents printed for reading, dissemination, and
  markup
• Paper in the office has increased!!
• Goal Deal with the flow of electronic and paper
  documents in an efficient and integrated manner
• Implication Unlike computer media, paper
  documents should be read by both the computer and
  people

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Short Tour of DIA

• Field started before digital computers could
  represent information traditionally appeared on
  paper
• Patents on OCR for telegraph and reading machines
  for the blind filed in the 19th century and
  working models demonstrated in 1916
• OCR on specially designed fonts used in 1950s
• First postal address reader installed in 1965
• OCRs to read scanned pages came into their own in
  1980s with the advent of the low cost
  microprocessors, bit-mapped displays, and
  scanners
• Large capacity storage devices have now ignited
  the field with the prospects of Digital Libraries
• Document imaging today is a billion dollar
  industry but document interpretation is only a
  small part of it

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Document Image Analysis
Graphical Processing
Textual Processing
Optical Character Recognition
Page Layout Analysis
Line Processing
Region and Symbol Processing
Skew, blocks, paragraphs
Lines, curves, corners
Filled regions
Text
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Current

• Processors getting faster
• Storage costs are down
• Pictures are typically 512 x 512 pixels
• Speech signals are typically 256 sample points
• Business letters are typically 2550 x 3300 pixels
  at 300 dpi
• Eng drawings are typically 34000 x 44000 pixels
  at 1000 dpi
• Digital libraries need WWW interface
• Information retrieval and search
• OCR accuracy on the rise
• Contextual models improved

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Document page
300 dpi, 8.5x11 in 255 gray X 3 color 2,550 x
3,300 pixels
Data capture
107 pixels
Pixel-level processing
7,500 character boxes, 15x20
pixels each
500 line and curve segments, 20 to 20,000
pixels each
10 filled regions 20x20
to 200x200 pixels each
Feature-level processing
10x5 region features
7500x10 character features
500x5 line and curve features
Text analysis recognition
Graphics analysis recognition
2 line diagrams, 1 company logo, etc.
1,500 words, 10 paragraphs, 1 title, 2 subtitles,
etc.
Document Description
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Document Image Analysis
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Document Taxonomy
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Postal Examples
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Forms
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Unconstrained Text
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Graphics Documents
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Personal DL
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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

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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

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CS 661 Class Schedule
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Grading

• Home Assignments and Quizzes
• 4 x 10 40 points
• schedule is tentative to preserve surprise
  element
• Based on class participation and paper handouts
• Midterm project
• Demo 10
• Report 15
• Final project
• Demo 10
• Report 25

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References

• Handbook of Character Recognition and Document
  Image Analysis, H. Bunke and PSP Wang (editors),
  World Scientific Press
• Document Image Analysis, Gorman and Kasturi ,
  IEEE Computer Society Press
• International Conference on Document Analysis and
  Recognition proceedings
• International Workshop on Document Analysis
  Systems proceedings
• Symposium on Document Image Understanding
  Technology