Keywords 
Distortion, Bleedthrough, Showthrough 
INTRODUCTION 
Ancient documents, Property documents and the like are scanned and converted to digital documents to store
them for future use. The scanned images might not be legible due to poor paper quality, spreading and flaking of ink
etc.There are many solutions available to restore the characters from these degraded documents. But to be more
efficient they need neat and readable inputs. The accuracy of today’s document recognition methods fail abruptly
when document image quality is poor slightly. In addition to this, significant improvement in accuracy on hard
problems now depends more on the size and quality of training sets as algorithms and hardware [1]. In order to
improve the performance, the proposed method combines algorithms such as Diffusion Method (DM), Independent
Component Analysis (ICA) Double SidedFlow Based Diffusion Method (DFDM) and NNKSOM. This is one of the
most challenging problems in OCR (Optical Character Recognition). 
REVIEW OF LITERATURE 
Numerous methods have been proposed to recognize Bleedthough problems. In order to reach the desired goal, an ample study of research outcomes in several related areas were surveyed. Techniques of this type are reported in Knox [2]
and Sharma [3] for reducing showthrough in scanned documents. The basic idea is presented in [2] and a restoration
technique using adaptive filtering is presented in [3]. Ophir and Malah [4] proposed a solution by taking showthrough
problem as a Blind Source Separation (BSS) problem, simultaneously estimating the images and mixing parameters. More
over they combine a Mean Squared Error fidelity term, incorporating the nonlinear mixing model and TotalVariation
(TV) regularization terms applied separately to each image. Leedham et al [5] attempted the recognition process with the
introduction of binarization methods with bleed through defects. Anna Tonazzini et al [6] and Emmanuelle et al [11] have
drawn more general approaches and statistical methods such as Independent Component Analysis (ICA) and Bline Source
Operation (BSS). Dubois and Anita [7] real samples are used for various distortion models. They have demonstrated the
recto and the flipped verso method and using a thresholdbased test to replace bleedthrough with a background level. Like
Dubois [7] more information regarding this can be accessed using [8]. Gang Zi [9, 10] proposed the only one other model
of distortion of bleed through type of defect taking the base of blurring and mixing techniques. Xiaowei et al [12]
introduced NN based approaches for showthrough problem as Blind Source Separation (BSS). Moreover, there are other
methods that combine several techniques such as segmentation, compression and decompression, stroke removal, etc [7, 13
and 14]. This work compares the statistical methods which are most promising with a novel approach based on the DMs.
Comparison is conducted from a fundamental point of view to enable a better understanding of the advantages and
disadvantages of the methods. Also, in addition to providing real samples that are obtained from [7, 8], a degradation model
is developed which is capable of generating an unlimited number of document images degraded by bleedthrough. This
model is discussed in the next section. As known so far, there is only one other degradation model based on blurring and
mixing technique [9, 10] for this type of defect. Finally, possible directions for the restoration and enhancement of very old
documents are offered which benefit from the advantages of both statistical and diffusion methods. 
ALGORITHMS USED 
Selection of appropriate method is the common technique used to determine certain initial activities to solve the
problems. Based on all those techniques, various techniques were briefly introduced in this section. 
STATISTICAL ALGORITHM 
Blind Signal Separation (BSS) application holds a remarkable place in statistical approach. In general BSS problem
often referred as blind source extraction (BSE) process. There appears to be something magical about blind source
separation were the original source signals are estimated without knowing the parameters of mixing and/or filtering
processes. In fact, without some prior knowledge, it is not possible to uniquely estimate the original source signals. In this
way the input images are considered as onedimensional arrays, which mean that the twodimensional input images are
ignored. This is not suitable when the sources are assumed to be independent. Then the next best approach is obviously
Independent Component Analysis (ICA). 
INDEPENDENT COMPONENT ANALYSIS (ICA) 
ICA is a newly developed statistical approach to separate unobserved, independent source variables from the observed
variables that are the combinations of these source variables. Although different types of functions are used in ICA
methods, the basic idea is simple. There is a cost function that determines the degree of independence of the computed
sources. To obtain a best estimation, maximization of the cost function is enough and also these methods assume a linear
relation between the source and the input. Using the standard ICA methodology, one can equate: 

Where X is a column matrix of mixed signals, A is a matrix representing the signal abundances and S is the column
matrix of the source signals. ICA usually starts from a preprocedure of “Whitening”. The key idea here is that if the signals
are independent, then they are uncorrelated, which in turn means that a procedure that decorrelates matrix X is a necessary
procedure for obtaining independent signals. That is, ICA is usually performed in two stages: 


The matrix W in this case is an orthonormal matrix that can be indeed considered as a rotation matrix in the n
dimensional space. The matrix Ω can be easily calculated on the basis of covariance matrix of X. Being a highorder
statistical technique, ICA outperforms the second order in the discrimination power. 
Advantages of ICA 
a) ICA usually starts from a preprocedure of “whitening”. 
b) The result of ICA processing information is near restoration to the true data. 
c) It does not add any additional information other than input data. 
Drawbacks of ICA 
a) This method requires an image of two sides of the document. 
b) Because of oneto one correspondence in ICA, recto and verso side of the document results will be very poor. 
c) This method is so sensitive in nature. If there is any shift of coordination pixels due to misalignment in the scanning
process. Entire result will not be clear. 
DIFFUSION METHOD (DM) 
Assume that due to some distortions the true data image is destroyed and the data must be corrected via exchange of
information between neighbors. These methods are based on the existence of a spatial correlation between the data of
neighboring pixels, So that each pixel is processed using the information of the surrounding pixels. This method removes
all weak structures that are surrounded by the neighboring pixels, which makes these methods very aggressive, even though
it is not applicable to source separation problems. However doublesided document images can be modified to make them
applicable to twosource separation problem of information (the recto and verso side). In addition to usual diffusion, some
diffusion process can be added which is called doublesided flowbased diffusion method (DFDM). 
Three ways to get Better Result 
DFDM method cancels out the effects of real physical distortion process that occurs over time. 
Additional diffusion processes actually separate the recto and verso side information to the background. 
Another Reverse diffusion process is included to get better result. This not only results in uniform and fluctuation
free background, but also speeds up the removal of interference by filling up the background patterns 
Advantages of Diffusion Method 
a) A resultant image of this method is fine and thin in structures. 
b) Twodimensional neighborhood nature collects information from the data of every pixel. Due to this, all nearby pixels
will be used in the process. 
c) It shows mutual local and global behavior. i.e., local behavior renders highly adaptable method to local variations
same way. 
Disadvantages of Diffusion method 
a) The computational cost in DM is approximately 10 times higher than ICA. 
b) Sometimes it leads to negative results as the originality of the document is altered. As a result of this recognition
results is low in some cases. 
c) Because of restoration problems, this method is less applicable. 
IMPLEMENTATION OF HYBRID TECHNIQUES 
In this section, we present some combined method of ICA, DM and Neural Network (NN) based KSOM (refer
Fig.4) to concentrate over Restoration and Enhancement, which a similar idea is seen in [19] but without NN. 
PROPOSED HYBRID ALGORITHM 
Algorithm: Double_Sided_Restoration 
Step 1: Implement any diffusion method over input image 
Step 2: Implement ICA method over DM_IMAGE 
Step 3: Name the resulted image as ICA_IMAGE_1 and ICA_IMAGE_2 
Step 4: Implement DFDM method over ICA_IMAGE_1 and ICA_IMAGE_2 
Step 5: Use NN technique, to classify information for ICA and DFDM 
Step 6: NN makes training for recognition 
Step 7: Recognition can be done on the Contentbased information 
Step 8: Results restore or enhance the input document image 
RESTORATION 
The coefficients of the source mixture in ICA are global. Here we modify the coefficients by taking potential approach
by including the results of the DM in the ICA method and include a term which computes the distance between the
estimated output and the DM results. 
ENHANCEMENT 
DFDM is a powerful tool for the enhancement and source separation. In general, it requires some source dominance in the inputs. Using ICA output as inputs to the DM will result in good enhancement. In this case, preseparation using ICA
will give two input images, which is very suitable for DFDM. Applying the DM then results in a very good enhancement
and total separation. This type of implementation results good even in previous failed ICA methods. Still there are some
defects in different colored inputs. This can be rectified using our hybrid techniques. 
CONTENT BASED INFORMATION 
Neural networks (NN) are richly connected networks of simple computational elements. The fundamental tenet of
neural computation or computation with NN is that such networks can carry out complex cognitive and computational
tasks. In addition, one of the tasks at which NN excels is the classification of input data into one of the several groups or
categories. In this paper NN based KSOM is used to classify data based on content of the information (hybrid technique).
The reason for using KSOM is, it is useful for visualizing lowdimensional views of highdimensional data. It differs from
the feed forward back propagations network in several ways. KSOM is trained in an unsupervised way. This means the
KSOM neural network is given input data but no anticipated output. The KSOM network begins to map the training
samples to each of its output neurons during training. More over KSOM does not use any sort of activation function, bias
weight. Output from the KSOM does not consist of the output of several neurons it is selected as a “Winner”. Often the
winning neurons represent groups in data that is presented to KSOM. Keeping all the above for the better result, we written
our hybrid equations and algorithm as follows. 

The proposed hybrid algorithm is implemented over Fig.1. The resultant output is shown in Fig. 5(a) and (b). 
CONCLUSION 
In this paper we have rewritten a few formulas for DFDM. The advantages and disadvantages of ICA and DFDM
method in restoring the doublesided documents are analyzed. Although ICA and DFDM produces highresolution results
in ordinary bleedthrough problems of ink seepage, it is also very aggressive and seriously modifies the input data. The
algorithm proposed combines these two approaches, were its efficiency being essential for applications presenting both a
high degree of dimensionality and time restrictions. We therefore conclude that combining NNKSOM with ICA and
DFDM restores and enhances document image more easily and results are very promising even in complex cases. A new
hybrid method is introduced to gain all the advantage on both ICA and DFDM. However the hybrid method requires one
more additional input. In order to fulfill this requirement ICA’s two output are taken as input image for further processes. 

Figures at a glance 




Figure 1 
Figure 2a 
Figure 2b 
Figure 3a 



Figure 3b 
Figure 4a 
Figure 4b 


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