A New Technique for Archiving Manuscript Documents

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World Journal of Computer Application and Technology 2(2): 29-33, 2014 http://www.hrpub.org
DOI: 10.13189/wjcat.2014.020201
A New Technique for Archiving Manuscript Documents
Muhammad Adeel Javaid
Member Vendor Advisory Council CompTIA
*Corresponding Author: ajaviad@gmail.com
Copyright © 2014 Horizon Research Publishing All rights reserved.
Abstract In the specific context of safeguard of old
documents encountered by specific problems of
conservation (damp patches, folds, tears, shade… etc.), we
are interested in the diagnosis of the methods of compression
of the two families: reversible (without loss) and irreversible
methods (with loss). Particularly, we are interested in the
current methods like RLE, Compression LZ and LZW,
Huffman, Deflate, CCITT Group 3 and 4, JPEG, JPEG2000,
png, and fractal Compression. The goal of this work is to
develop a semi-automatic tool able to quantify the quality of
a compression of old documents. Then, can answer
objectively for the choice of a compression method from a
collection of images. The choice of this collection must be
representative in terms of a number of images and typology
of images of the same context, like here the old documents.
In this paper, we propose a semi-automatic method that
operates at the first time on a supervised classification of the
original image to raise K groups with distinguished
characteristics. The choice of K is previously determined by
the user: writing, bottom, tear….etc. In the second step, we
calculate the movements (intra class and inter class). The last
step consists in calculating of the degradation measurement.
Keywords Classification Application, Archive
Application, Manuscript Archival Application
1. Introduction
All treatment applied on digital images must be validated
by a process of quality. In the context of images compression,
and especially concerning the compression of the images of
ancient manuscripts, this process is based on measures of
the quality of the rebuilt image. For applications in which
images are finally degraded, the only correct method for
measuring of the visual quality of image is by the subjective
assessment. In practice this assessment is realized by
subjective criteria such as the Mean Opinion Score (MOS)
[1]. This measure is still heavy, and very costly. However,
this assessment cannot be in any case used in a scheme of
image coding [2]. In order to avoid this problem, it is
necessary to assess the quality of the compressed images,
this is realized by MSE: Means Square Error or PSNR: Peak
Signal Noise Ratio. Until today the measure of PSNR is
considered as the most used criteria for evaluating the
quality in image processing, however, the PSNR is a
quantitative measure and need sometimes a subjective
assessment of the degradation.
From quantitative measures, the objective methods
assessing the perceptual quality of the image have
traditionally attempted to measure the visibility of the errors
between an image deteriorated and a reference image using a
set of properties inspired by the human visual system (HVS)
[3][4][5]. Our contribution is interested in distinguish in
groups the elements of the image to associate a strong
weighting for the relevant groups, and shall be less important
for the groups less relevant according to HVS.
Our method is going to take advantage of both types
(objective and subjective), then we developed a
semiautomatic tool capable of quantifying the quality of a
compression in front of archive documents. So, it allows
answering objectively the choice of a method of images
compression. The choice of this collection must be
representative in terms of number of images and typology of
images of the same context, as here the old documents.
2. Process of Classification
The techniques of automatic classification are intended to
produce groups of objects by an algorithmic step. The
objects to be classified are in general individuals whom we
wish to divide into disjoined homogeneous classes. The
methods used are different and many but the total step
articulate on three principal axes:
 Identify the criteria of selection of the individuals
 Calculate the similarities between the individuals
 Use the heuristic ones to classify the individuals.
Moreover, we distinguish two types of classifications:
 Supervised classification: the classes and their
properties are known with priori. A semantic is
generally associated to each class.
 Unsupervised classification: semantics associated
with the classes is more difficult to determine. The
classes are founded on the structure of the elements.
These methods are called methods of clustering or
methods of regrouping.

30 A New Technique for Archiving Manuscript Documents
Here, we are informed on the characteristics of the image.
Supervised classification is more suitable.
Figure1. Example of image
For example of the figure 1 we observe that are three
classes in the image: writing, bottom, tear.
3. Process of Measurement
3.1 Supervised Classification
Our algorithm uses a method of classification overseen
more exactly the method of distance minimum. With this
method, the rule of decision for the affectation of a pixel in
a class is the distance minimum between the value of the
pixel and the center of a cloud of point representing a class.
So pixels are allocated to the class to which the average is
closest. This nearness spatial and often are measured by
the Euclidian distance.
Figure 2. pixels affectation
Iterative algorithm Initialization
 Choice of a distance suited (in our work it is the
Euclidian distance).
 Read the entry classes
 Calculate the center of gravity of each class
 Calculate the distance between the individual and
the centers of the classes
 Affectation of every individual has the class of the
closest center of gravity
 Pass in the following pixel
Our function of classification is based on the calculation
of the distances between every pixel and the centers of
classes; the distances are calculated by the formula of the
distance of Manhattan:
=((,,)−(,,))+((,,)−
(,,))+((,,)−(,,)) (1)
3.2. Movements Measurement
After every compression we redo the same process
(supervised classification) and we calculate the movements
of pixels. We notice two types of movements.
Intra class: the pixel moves in the same class.
Inter class: the pixel moves towards another class.
Figure 3. Pixels movements
Iterative algorithm
 Applied the function of classification to both images
(original and compressed).
 Read the classes (the classes in that case are the
result of the previous function)
 While N ° of class <=3
o While we did not arrive at the end of the picture
 Read N of pixel (the current
compartment of the 1st lines)
 Look for the same element
in
three
classes of the image tablet.
 Compare both distances If they are
different and have the same N ° of class
we are in the case of a movement intra
classy.
 If they are different and have the same N °
of class we are in the case of an Intra
class movement.
 The movement is calculated by the
differences between both distances.
o Pass in the following element.
 Pass in the following class
In Our work, the movement of pixels plays an important
role to measure the quality of the compression, because our
measure is calculated from the calculation of the movements
of pixels.

World Journal of Computer Application and Technology 2(2): 29-33, 2014 31
3.3. Measure of degradation
After the stage of the movements’ measurement, it still
remains to us the last stage, calculating our measure. Our
measure is calculated by the following formula:
(2)
4. General schema
We carry out our regrouping on the basis of property of
coding of the color of the image. In this first version in mode
RGB (component Red, Green, Blue). The problem of
regrouping in this case, can be seen like a problem of
separation of a set of pixels in a certain number of groups
according to their components RGB.
Figure 4. General schema of the proposed model of measure
According to the schema given (figure 4), let us recall the
architecture which we propose for a tool for evaluation of the
quality of compression of archival manuscript documents:
a) Starting of corpus of images, a decider must bring the
answer on which method compression to choose? Our
system known as “semi-automatic” makes it possible to
answer this question with objective evaluations (movements
measurement) and subjective ones (notations of the decision
maker). Of course, these evaluations are operated on a subset
of the selected corpus in terms of representativeness of the
typology of documents treated.
b) Each image of the subset must initially pass by the
phase known as automatic, then the phase known as
semi-automatic insofar as it utilizes human (appraiser).
 Automatic phase: This phase consists in making a
distinctive regrouping of pixels, i.e.: the pixels
relating to the writing will form the same group, those
of the bottom of the image another group; it will be
the same for the tasks, shades, and other spots of
conservation. This operation called classification is
ensured by a process explained in section 2. Remain to
determine the number of groups to build. (three in our
case)
 Semi-automatic phase: Once the various groups are
well distinguished in the preceding phase. An
appraiser gives more weight to the important groups
for the legibility of the document like the writing and
the bottom. The task of compression should not
degrade these groups. By opposition, the groups
translating of the noise are slightly balanced in terms
of importance; a degraded compression is probably
without incidence.
c) Then it acts to measure degradation after the choice of a
method of compression. Several methods should be tested
to have the best compromise between the qualitative factor
and the size one, after compression.
5. Movements Measurement of Pixels
for Several Methods of Compression
We constituted 6 documents to lead 6 sets of tests on the
model which we propose. The tested methods of
compression are:
 GIF,
 PNG-8,
 PNG-24,
 JPEG,
 JPEG2000
Table 1. Number of intra-class movements
Image
(Original size) PNG TIFF Jpeg Jpeg2000
Im1 (98 ko) 18720 18720 18515 164
Im2 (119 ko) 37862 37862 36776 55
Im3 (178 ko) 52143 52143 53804 117
Im4 (253 ko) 63752 63752 73325 132
Im5 (545 ko) 70725 70725 70235 56
Im6 (1136 ko) 142973 142973 141172 412
Min 18720 18720 18515 55
Max 142973 142973 141172 412
Average 64363 64363 65638 156
Three tables of digital tests (intra class, inter class and
measure of degradation) should give on the method of
compression to be chosen. Tables (individuals / variables)
below which individuals represent the images tests and
variables represent the methods of compression, give
numerical values representing the number of pixels having
made of the movements after compression. For every
method of compression, it is important to observe the values
averaged on the sample of images, as well as the values "
min " and "max" translating an interval of fluctuation which
concretely gives an idea onto the correlation of the method

32 A New Technique for Archiving Manuscript Documents
of compression towards the nature of the image.
The results indicate initially, a correlation between the
size of the original and degradation. Certainly, more the
image is voluminous; more the number of movements are
high.
We also notice that the number of movement increases
with regard to the method of compression, what indicates
that our model is really sensitive to the slightest degradation
which affects the image after a compression.
The following table (table 2) shows Number of interclass
movements
Table 2. Number of interclass
movements
Image
(original size)
PNG TIFF Jpeg Jpeg2000
Im1 (98 ko) 11881 11881 13153 2
Im2 (119 ko) 2017 2017 983 1
Im3 (178 ko) 6162 6162 5271 9
Im4 (253 ko) 21332 21332 9848 2
Im5 (545 ko) 1908 1908 1863 1
Im6 (1136 ko) 1989 1989 1909 7
Min 1908 1908 983 1
Max 21332 21332 13153 9
Moyenne 7548 7548 5668 4
Table 3. Degradation measure of the compression quality according to the
compression method
Image
(original size)
PNG
TIFF
Jpeg
Jpeg2000
Im1 (98 ko)
6.8775
6.8775
5.4353
1.1446
Im2 (119 ko)
15.5128
15.5128
6.7238
1.1429
Im3 (178 ko)
10.7821
10.7821
6.5654
1.1429
Im4 (253 ko)
31.0577
31.0577
15.6804
1.1343
Im5 (545 ko)
3.8901
3.8901
2.1124
1.0877
Im6 (1136 ko)
2.0417
2.0417
1.1885
0.5416
Min
2.0417
2.0417
1.1885
0.5416
Max
31.0577
31.0577
15.6804
1.1446
Moyenne
11.6937
11.6937
6.2843
1.0323
The table 2 shows well the difference marked between the
methods as the JPEG and the JPEG 2000 and others. We
know the power of the JPEG model as an irreversible model
but the loss of which is planned in a sensible way, by taking
into account capacities of the human eye. For our model it’s
rather clear that the JPEG and JPEG2000 models presents
fewer movements inter class than the rest of the methods,
what is already a good comparative measure.
The table 3 shows the stability of the tests, what allows us
to conciliate for the same compression ratio as 2 methods
(PNG, TIFF) remain nearby and less powerful. The interest
is carried for the JPEG and JPEG 2000 methods which give
good results in that case of tests. The economic aspect is also
determining to slices between these two formats
6. Variation of the Degradation
According To Compression Ratio
The following table (table 4) shows the variation of the
average of degradation according to 5 selected levels of
compression (1:12, 1:24, 1:30, 1:40, 1:50)
Table 4. Variation of degradation according to
the
compression
ratio
Taux de compression
PNG
TIFF
Jpeg
Jpeg2000
10%
3.5891
3.5891
2.5236
1.1115
20%
4.1236
4.1236
2.6129
1.1219
30%
4.6325
4.6325
3.0455
1.1205
40%
8.6523
8.6523
5.3256
1.1311
50%
10.5891
10.5891
6.2351
1.3228
According to our model, the table shows well that
degradation believes very quickly with the increase in the
compression ratio, in particular for methods GIF, PNG-8,
and PNG-24. In less marked, it will be the same for method
JPEG. Method JPEG 2000 after a compression 1:50 one
remains with a tiny degradation. Clearly, the effectiveness of
this method was already proven in the literature, it is not the
object of this section. This being said, that makes it possible
to note the relevance of the model of evaluation which we
set up.
7. Conclusion
We presented a new method of measure of the
compression quality, based on a supervised classification
and movement’s measurement.
The results of the evaluation of this method show initially,
logic of the model of evaluation compared to the results
noted through other method. Also, the numerical results
coming from the model reflect the conclusions noted in the
literature for each studied model. Some experiments control
highlighted the major impact of certain post processing on
the visibility of the artefacts of compression. The principal
post processing of which the use must be supervised in
combination with compression is the reinforcement of
contour (for the description of the writing).
These evaluations are positive as for the application of the
measure of the compression quality. They deserve to be
supplemented by comparative studies complementary
compared to the purely objective or purely subjective
methods on benchmarks of the literature.

World Journal of Computer Application and Technology 2(2): 29-33, 2014 33
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