Extraction of text data and hyperlink structure from

scanned images of mathematical journals

Ann Arbor, March 19, 2002Masakazu Suzuki(Kyushu University)

Outline of the talk

1. Motivation of our project INFTY.

2. 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.

1. 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.

1. 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

1. 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

Clearly printed documents 400～ 600DPI

1. 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.

2. Goal

Text data with coordinates → Title, Author info., …, References, Keywords,

Hyperlink structure. Full recognition including mathematical expre

ssions and logical structure of the document → Reproduction of Contents, Automatic translation, Verification

3. Case of Mathematical Journals

After 1960 ：　　 Good quality in printing and paper1940 ～ 1960 ：　　 Low quality papers → noize18C, 19C, beginning of 20C ：　１． Sometimes stained yellow → noize　２． Use of fonts (beautiful fonts) different

from recent ones

3. What are difficult?

1. Noise reduction.

2. 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.

3. Recognition Process Flow

1. Skew correction and Noise reduction

2. Layout analysis (Block segmentation),

3. Segmentation of text area into lines,

4. Character recognition in text area

5. Segmentation of text/math areas,

6. Character and symbol recognition in math. area,

7. Structure analysis of math. expressions,

8. Correction of text/math segmentation,

9. Output.

4. Character Recognition

1. Sample image database

of special symbols.

2. Touched characters and broken characters

in mathematical expressions.

4. Character Recognition

1. 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.

4. 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.

4. Character Recognition

1. 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, …?

5. Layout Analysis

5. Layout Analysis

5. Layout Analysis

5. Layout Analysis

5. 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.

6. Line Segmentation

6. Line Segmentation

6. Line Segmentation (sample)

6. Line Segmentation (sample)

6. Line Segmentation (sample)

6. Line Segmentation (sample)

7. Text/Math Segmentation

Math MathText Text

7. 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 　

7. Text/Math Segmentation

Difficulties in criteria: Isolated letter “a” in italic font, Isolated Capital letters, (Initial, etc.) Numerals (Items, Citations, Section nu

mbers, Theorem numbers, or Numbers in math. Expressions?)

Abbreviations (i.e., e.g., etc.)

7. Text/Math Segmentation

Examples …

See the demonstration html files: 1. Comment_Math_Helv_69_039_048.html 2. Comment_Math_Helv_71_060_069.htmlThese 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)

aa

bb

xx22

dd xx

8. Structure Analysis of Mathematical Expressions

8. Structure Analysis of Mathematical Expressions

aa

bb

xx22

dd xx

n

iia

0ii 00

nn

aa ii

8 Structure Analysis of Mathematical Expressions

9. Output format

Intermediate XML format　　　　　　↓

XML format as final result output　　　　　　↓

Embedding of hyper Link structure 　　　　　　↓

LaTeX, HTML, etc.

10. Work Flow of Digitization

Pre-Processing for image files:- Erase large peripheral noises,- Erase figure areas and table areas

Get the recognition results using Ando’s interface,

Extract various data which you need from our XML output.

INFTY α-test cite

Currently, we have an α-test cite of our system:

http://133.5.158.104/Infty/index.htmlIf you upload TIF files of scanned page i

mages of mathematical paper, (TIF Grade3, 400DPI/600DTI),

Then, you can download the recognition results, either in LaTeX format or in HTML format.

Further 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.

Database

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).

INFTY

Thank you.

Masakazu Suzuki Faculty of Mathematics, Kyushu Universitysuzuki@math.kyushu-u.ac.jp