Title: Extraction of text data and hyperlink structure from scanned images of mathematical journals
1Extraction of text data and hyperlink structure
from scanned images of mathematical journals
- Ann Arbor, March 19, 2002
- Masakazu Suzuki
- (Kyushu University)
2Outline of the talk
- Motivation of our project INFTY.
- What are the goal?
- 3. What are the difficulties in mathematical
document recognition? - 4. Present state of our system, with demo.
- 5. Work flow of retrodigitization
- 6. Alpha-Test Home Page
- 7. Conclusion.
31. INFTY
INFTY the OCR system (document reader), -
for mathematical documents, - developed in my
laboratory in Kyushu University, - in
cooperation with the section of OCR in Toshiba
Corporation e-Solution Company, specially with
the developer team of the Toshiba document
reader called ExpressReader Pro.
41. INFTY
- Recognition of scanned page images of (English /
Japanese) mathematical documents - Intuitive and easy user interface to correct the
recognition results - Output of the recognition results in XML, MathML,
LaTeX, and Braille codes
51. INFTY
- Clearly printed documents
- 400600DPI
- Recognition of scanned page images of (English /
Japanese) mathematical documents - Intuitive and easy user interface to correct the
recognition results - Output of the recognition results in XML, MathML,
LaTeX, and Braille codes
61. Motivation
- Help visually impaired students / people to study
/ work in scientific fields - Retro-digitization of mathematical journals to
include them in a searchable digital libraries.
72. Goal
- Text data with coordinates ? Title, Author
info., , References, Keywords, - Hyperlink structure.
- Full recognition including mathematical
expressions and logical structure of the
document ? Reproduction of Contents, Automatic
translation, Verification
83. Case of Mathematical Journals
- After 1960
- Good quality in printing and paper
- 1940 1960
- Low quality papers ? noize
- 18C, 19C, beginning of 20C
- 1.Sometimes stained yellow ? noize
- 2.Use of fonts (beautiful fonts) different
- from recent ones
93. What are difficult?
- Noise reduction.
- Character and symbol recognition.
- 3. Layout analysis
- 1. Block segmentation
- 2. Line segmentation
- 3. Segmentation of Text / Math Areas
- 4. Structure Analysis of mathematical
expressions. - 5. Logical structure analysis.
103. Recognition Process Flow
- Skew correction and Noise reduction
- Layout analysis (Block segmentation),
- Segmentation of text area into lines,
- Character recognition in text area
- Segmentation of text/math areas,
- Character and symbol recognition in math. area,
- Structure analysis of math. expressions,
- Correction of text/math segmentation,
- Output.
114. Character Recognition
- Sample image database
- of special symbols.
- 2. Touched characters and broken characters
- in mathematical expressions.
124. Character Recognition
- Sample image database
- of special symbols.
- 2. Touched characters and broken characters
- in mathematical expressions.
It is a very hard work to collect a large number
of sample images of mathematical symbols.
134. Character Recognition
- Currently, INFTY recognizes, in addition to
- alphanumeric characters and Greek characters,
- about 250 kinds of other mathematical symbols.
- It distinguishes well the difference of italic
font - and upright font of alpha numeric characters.
- However, the distinction of the boldface from
- normal font is left to the future research.
144. Character Recognition
- Sample image database
- of special symbols.
- 2. Touched characters and broken characters
- in mathematical expressions.
In text area, 1. DP Method, 2.
Bi-grams, Tri-grams, 3. Word Dictionaries,
etc. However, in math area, ?
155. Layout Analysis
165. Layout Analysis
175. Layout Analysis
185. Layout Analysis
195. Layout Analysis
- Currently, Infty supports only graphical layout
analysis. - Logical structure analysis, such as titles,
author information, section/subsection structure,
indexing, theorem description areas, citation
links, etc. - are all left to future works.
206. Line Segmentation
216. Line Segmentation
226. Line Segmentation (sample)
236. Line Segmentation (sample)
246. Line Segmentation (sample)
256. Line Segmentation (sample)
267. Text/Math Segmentation
Math
Math
Text
Text
277. Text/Math Segmentation
Segmentation of text/math areas, using character
recognition results of ExpressReader Pro
Character ans symbol recognition in Math. Area
and the structure analysis of math. expressions
Correction of text/math segmentation
287. Text/Math Segmentation
- Difficulties in criteria
- Isolated letter a in italic font,
- Isolated Capital letters, (Initial, etc.)
- Numerals (Items, Citations, Section numbers,
Theorem numbers, or Numbers in math.
Expressions?) - Abbreviations (i.e., e.g., etc.)
297. Text/Math Segmentation
- Examples
- See the demonstration html files
- 1. Comment_Math_Helv_69_039_048.html
- 2. Comment_Math_Helv_71_060_069.html
- These are the samples automatically generated by
our recognition system INFTY, on March 19, 2002
at Ann Arbor. They includes some errors and show
the present state of our system, since no manual
correction is processed on the results. The
hyperlinks are also generated by the system. - To look the results correctly, you have to
install INFTY fonts - Infty Font 1.TTF, Infty Font 2.TTF,
Infty Font 3.TTF, - in your computer, before opening these html
files. - (Notes added on April 4th,2002 at
Fukuoka)
308. Structure Analysis of Mathematical Expressions
318. Structure Analysis of Mathematical Expressions
328 Structure Analysis of Mathematical Expressions
339. Output format
- Intermediate XML format ?
- XML format as final result output ?
- Embedding of hyper Link structure ?
- LaTeX, HTML, etc.
3410. Work Flow of Digitization
- Pre-Processing for image files- Erase large
peripheral noises,- Erase figure areas and table
areas - Get the recognition results using Andos
interface, - Extract various data which you need from our XML
output.
35INFTY a-test cite
- Currently, we have an a-test cite of our system
- http//133.5.158.104/Infty/index.html
- If you upload TIF files of scanned page images of
mathematical paper, (TIF Grade3, 400DPI/600DTI), - Then, you can download the recognition results,
either in LaTeX format or in HTML format.
36Further problems
- Further Improvement of recognition rate of
characters, - Further Improvement of layout analysis,
- Recognition of touched characters and broken
characters, - Logical structure analysis of the document,
- Automatic detection of keywords, etc.
37Database
- In order to progress further the research of
mathematical/scientific document recognition, we
need a large scale of database of page image
files with correct recognition results keeping
the coordinates correspondence of each character
with the original image (ground truth).
38INFTY
Masakazu Suzuki Faculty of Mathematics, Kyushu
Universitysuzuki_at_math.kyushu-u.ac.jp