1. Title of Database: Pen-Based Recognition of Handwritten Digits
		Original, unnormalized version
2. Source:
	E. Alpaydin, F. Alimoglu
	Department of Computer Engineering
	Bogazici University, 80815 Istanbul Turkey
	alpaydin@boun.edu.tr
	September 1998

3. Past Usage:
	F. Alimoglu (1995) Combining Multiple Classifiers for Pen-Based
	Handwritten Digit Recognition, 
	MSc Thesis, Institute of Graduate Studies in Science and 
	Engineering, Bogazici University.
	http://www.cmpe.boun.edu.tr/~alimoglu/alimoglu.ps.gz

	F. Alimoglu, E. Alpaydin, "Methods of Combining Multiple Classifiers 
	Based on Different Representations for Pen-based Handwriting
	Recognition," Proceedings of the Fifth Turkish Artificial 
	Intelligence and Artificial Neural Networks Symposium (TAINN 96), 
	June 1996, Istanbul, Turkey.
	http://www.cmpe.boun.edu.tr/~alimoglu/tainn96.ps.gz

	
4. Relevant Information:

	We create a digit database by collecting 250 samples from 44 writers.
	The samples written by 30 writers are used for training,
	cross-validation and writer dependent testing, and the digits 
	written by the other 14 are used for writer independent testing. This
	database is available in the UNIPEN format.

	We use a WACOM PL-100V pressure sensitive tablet with an integrated 
	LCD display and a cordless stylus. The input and display areas are
	located in the same place. Attached to the serial port of an Intel 
	486 based PC, it allows us to collect handwriting samples. The tablet
	sends $x$ and $y$ tablet coordinates and pressure level values of the
	pen at fixed time intervals (sampling rate) of 100 miliseconds. 

	These writers are asked to write 250 digits in random order inside 
	boxes of 500 by 500 tablet pixel resolution.  Subject are monitored 
	only during the first entry screens. Each screen contains five boxes
	with the digits to be written displayed above. Subjects are told to
	write only inside these boxes.  If they make a mistake or are unhappy
	with their writing, they are instructed to clear the content of a box 
	by using an on-screen button. The first ten digits are ignored 
	because most writers are not familiar with this type of input devices,
	but subjects are not aware of this. 

	In our study, we use only ($x, y$) coordinate information. The stylus
	pressure level values are ignored. The raw data that we capture 
	from the tablet consist of integer values between 0 and 500 
	(tablet input box resolution). 

	pendigits-orig contain original, unnormalized data. pendigits is the 
	normalized and resampled version where all inputs are of the same
	length. Here because of speed or the digit, feature vectors may be of
	different lengths, e.g., '1' is shorter than '8'.

5. Number of Instances
	pendigits-orig.tra	Training	7494
	pendigits-orig.tes	Testing		3498
	
	The way we used the dataset was to use first half of training for 
	actual training, one-fourth for validation and one-fourth
	for writer-dependent testing. The test set was used for 
	writer-independent testing and is the actual quality measure.

	pendigits.tra divides into
		Actual training	set	3748
		Validation		1873
		Writer-dependent test	1873

6. Number of Attributes
	Input size depends on writing speed and time and is not fixed
	+1 class attribute

7. For Each Attribute:
	The data is in the UNIPEN format. See
	I. Guyon UNIPEN 1.0 Format Definition, 
	ftp://ftp.cis.upenn.edu/pub/UNIPEN-pub/definition/unipen.def
	1994

8. Missing Attribute Values
	None

9. Class Distribution

          classes
          0    1    2    3    4    5    6    7    8    9
tra     384  390  392  370  391  375  351  375  363  357 Tot 3748 
cv      209  201  201  163  185  163  191  196  178  186 Tot 1873 
wdep    187  188  187  186  204  182  178  207  178  176 Tot 1873 
windep  363  364  364  336  364  335  336  364  336  336 Tot 3498