The resources for this dataset can be found at https://www.openml.org/d/30

Author:   
Source: Unknown -   
Please cite:   

1. Title of Database: Blocks Classification
 2. Sources:
    (a) Donato Malerba
        Dipartimento di Informatica
        University of Bari
        via Orabona 4
        70126 Bari - Italy
        phone: +39 - 80 - 5443269
        fax: +39 - 80 - 5443196
        malerbad@vm.csata.it
    (b) Donor: Donato Malerba
    (c) Date: July 1995
 3. Past Usage:
    This data set have been used to try different simplification methods
    for decision trees. A summary of the results can be found in:
 
    Malerba, D., Esposito, F., and Semeraro, G.
    "A Further Comparison of Simplification Methods for Decision-Tree Induction."
    In D. Fisher and H. Lenz (Eds.), "Learning  from Data: 
    Artificial Intelligence and Statistics V", Lecture Notes in Statistics,
    Springer Verlag, Berlin, 1995.
 
    The problem consists in classifying all the blocks of the page
    layout of a document that has been detected by a segmentation
    process. This is an essential step in document analysis
    in order to separate text from graphic areas. Indeed, 
    the five classes are: text (1), horizontal line (2),
    picture (3), vertical line (4) and graphic (5).
    For a detailed presentation of the problem see:
 
     Esposito F., Malerba D., & Semeraro G.
   Multistrategy Learning for Document Recognition
          Applied Artificial Intelligence, 8, pp. 33-84, 1994
 
    All instances have been personally checked so that
    low noise is present in the data.
 
 4. Relevant Information Paragraph:
 
    The 5473 examples comes from 54 distinct documents. 
    Each observation concerns one block. 
    All attributes are numeric.
    Data are in a format readable by C4.5.
 
 5. Number of Instances: 5473.
 
 6. Number of Attributes 
 
    height:   integer.         | Height of the block.
    lenght:   integer.     | Length of the block. 
    area:     integer.    | Area of the block (height * lenght);
    eccen:    continuous.  | Eccentricity of the block (lenght / height);
    p_black:  continuous.  | Percentage of black pixels within the block (blackpix / area);
    p_and:    continuous.        | Percentage of black pixels after the application of the Run Length Smoothing Algorithm (RLSA) (blackand / area);
    mean_tr:  continuous.      | Mean number of white-black transitions (blackpix / wb_trans);
    blackpix: integer.    | Total number of black pixels in the original bitmap of the block.
    blackand: integer.        | Total number of black pixels in the bitmap of the block after the RLSA.
    wb_trans: integer.          | Number of white-black transitions in the original bitmap of the block.
 
 
 
 7. Missing Attribute Values:  No missing value.
 
 8. Class Distribution: 
 
                                            Valid    Cum
    Class               Frequency  Percent  Percent  Percent
  
 text                      4913     89.8     89.8     89.8
 horiz. line                329      6.0      6.0     95.8
 graphic                     28       .5       .5     96.3
 vert. line                  88      1.6      1.6     97.9
 picture                    115      2.1      2.1    100.0
                                 -------  -------  -------
                         TOTAL      5473    100.0    100.0
 
 Summary Statistics:
 
 Variable      Mean    Std Dev   Minimum   Maximum   Correlation 
 
 HEIGHT       10.47      18.96         1       804         .3510
 LENGTH       89.57     114.72         1       553        -.0045
 AREA       1198.41    4849.38         7    143993         .2343
 ECCEN        13.75      30.70      .007    537.00         .0992
 P_BLACK        .37        .18      .052      1.00         .2130
 P_AND          .79        .17      .062      1.00        -.1771
 MEAN_TR       6.22      69.08      1.00   4955.00         .0723
 BLACKPIX    365.93    1270.33         7     33017         .1656
 BLACKAND    741.11    1881.50         7     46133         .1565
 WB_TRANS    106.66     167.31         1      3212         .0337
 

 Information about the dataset
 CLASSTYPE: nominal
 CLASSINDEX: last