1. Web Document Image Analysis (OCR) Servers
Richard Fateman
University of California
Berkeley, CA
USA
11/14/2018
Web-OCR R. Fateman

2. Web-OCR Project Goals
Provide OCR facilities for all users of a digital library:
Enter new private documents
Enter public documents
Correct, annotate, index documents
Minimal software/hardware (scanner) requirements at user end
Highest technical quality
11/14/2018
Web-OCR R. Fateman

3. Rationale:
As a practical matter, a digital library can be more useful if submissions need not enter through central control.
The user cannot be expected to maintain expensive hardware or software.
Expertise is shared:
User knows content
Server provides special services like OCR
11/14/2018
Web-OCR R. Fateman

4. Outline of Operation User Scans in a document
Produced by low-cost flatbed scanner
HP Capshare hand-held scanner
User sends a TIFF (image format) document to our web site
User Interacts via web browser to correct, catalog, annotate, index
11/14/2018
Web-OCR R. Fateman

5. Sample Input Page
Let me know what TIF file to process. If you leave this blank, I'll use my own test page.
Optionally, tell me your name or provide some identifier by editing this box:
Most images have noise. We suggest removing all speckles whose area is less than say pixels. You may chose another threshold.
Browse..
11/14/2018
Web-OCR R. Fateman

6. Sample Input, continued (1)
If you check this box I will deskew the picture first. Text aligned to the horizontal axis is easier to recognize.
If you check this box I will show you a reduced-resolution gif of your page.
If you check this box I will proceed to compute connected components (con-comps).
If you check this box I will return a short list of (x,y,width height) of con-comps
If you check this box I will show the first pixel maps of con-comps 
11/14/2018
Web-OCR R. Fateman

7. Sample Input, continued (2)
If you check this box I will continue the processing and group the connected components into clusters. I need some measure of tolerance to allow slightly different characters to fit into the same cluster. I'm guessing that is a good start.
If you check this box I will show you a selection of the most populous clusters and up to two entries for each them.
If you check this box I will show the result of the OCR in text form [This is not installed yet].  
11/14/2018
Web-OCR R. Fateman

8. Sample Output
You didn't send a file so I'm using my own sample tiff image.
In processing your form, we found these requests
item: ("thefile" . " ")
item: ("yourname" . "Anonymous ScanFan ")
item: ("Noise" . "30 ")
item: ("showgif" . "on ")
item: ("cc" . "on ")
item: ("showboxes" . "on ")
item: ("pixelmaps" . "on ")
item: ("pixelmapcount" . "10 ")
item: ("clustering" . "on ")
item: ("clustervalue" . "90000 ")
item: ("clustercount" . "10 ")
item: ("OCRtxt" . "on ")
Hello Anonymous ScanFan !
I found 51 horizontal breaks.
11/14/2018
Web-OCR R. Fateman

9. Sample Output: Review of TIFF
11/14/2018
Web-OCR R. Fateman

10. Sample Feedback on Characters
There are 2100 components
After filtering noise smaller than 30 we have 2052 components
Here are the dimensions of the first 10 boxes
;;(x y width height)
(( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ))
Here are the first 10 components of area 30 or more, as pictures
11/14/2018
Web-OCR R. Fateman

11. Sample Feedback on Clusters
Clustering tolerance parameter give 127 clusters of sizes ( )
Here are patterns of the first 10 clusters at tolerance and their first elements, with the count of entries
11/14/2018
Web-OCR R. Fateman

12. More clusters: t l v
Our program initially doesn’t know what these are; it has to be taught.
11/14/2018
Web-OCR R. Fateman

13. Improving our software
More systematic approach
Zoning, line and word segmentation
Possibly interactive (user-supplied) zones
More thorough noise removal
Better user interface?
Java? Javascript? Pop-up windows?
11/14/2018
Web-OCR R. Fateman

14. Significant Features of our OCR
Disadvantages
Must maintain a server
Must keep track of technology: changes to html, browsers
Advantages
Full control of source
We can (in principle) CALL Commercial OCR APIs
We can keep the TIFF images
Full control of output format (MVD and more)
11/14/2018
Web-OCR R. Fateman

15. Technology we use Machine independent (Sun, Intel, HP)
Lisp
Tiff library (for reading TIFF files)
Gd library (for producing GIFs)
HTML generator (lisp macros)
Server software
Could be ported to an entirely free base on Linux, GCL, G++
11/14/2018
Web-OCR R. Fateman

16. Alternatives: commercial
Advantages
Omni-font, pre-trained heuristics etc.
High accuracy (if you believe the press releases)
Many output forms (ASCII, html, word.doc, xdoc)
Commercial support available ($$)
Disadvantages
Non-modular
Not (yet) web-interactive
Inflexible character sets, mostly business usage
11/14/2018
Web-OCR R. Fateman

17. Current Status (July, 2000) Mostly one-person project to date
5 Hand-held HP Capshare scanners now available for student/researcher input
Laptop “feed” machines scheduled for delivery soon
OCR graduate class scheduled for Fall, 2000 at Berkeley
A commercial conversion hardware/ software solution from Xerox is being investigated.
11/14/2018
Web-OCR R. Fateman