Illumination Reduction for Camera Captured Documents Using Block

2011 International Conference on Communication Systems and Network Technologies

Illumination Reduction for Camera Captured Documents using Block processing and Homomorphic Filtering Technique H.K.Chethan,Research Scholar

G.Hemantha Kumar,Professor

DoS in Computer Science University of Mysore,Mysore Karnataka, INDIA 570005 Email: [email protected]

DoS in Computer Science University of Mysore,Mysore Karnataka, INDIA 570005 Email: [email protected]

Abstract—When acquiring document image using portable camera like mobile and other devices like HIDs in uncontrolled environment often, exhibits various forms of distortions to captured image , Which in turn degrades the quality of the image. Data quality of the image is highly affected by illumination. Therefore removal of such illumination and increase of data quality is necessary for human perception and machine recognition. In this paper, We propose a novel methodology for Illumination correction and data quality improvement for camera captured documents. The proposed methodology uses Homomorphic and Morphological filtering techniques to reduce illumination and some efficient binarization and enhancement techniques to improve data quality. Experimental results on camera captured documents shows that results obtained are encouragingly reliable, effective and efficient compared with the existing proven methods.

into sub-images, and performing homomorphic filtering on each sub-image individually, the performance can be significantly improved. Actually, we will show that by combining two filtered sub-image representations we can improve overall performance even further. The paper is organized as follows:

Keywords: Illumination, Morphological, Binarization, Homomorphic Filtering, Data Quality, HIDs. Fig. 1.

I. I NTRODUCTION

in Section 2 we shall give a detailed theoretical background of homomorphic. filtering: in Section 3 we shall give brief description of steps followed by our proposed modifications; in Section 4 we shall deal with feature extraction using PCA; in Section 5 we shall deal with Classification using Multi class SVMs;in Section 6 we present our dataset developed for experiments:Section 7 shows the experimental results of some standard method compared to our proposed method; Section 8 concludes the paper and lists possible improvements and further work.

Cameras are Ubiquitous in nature. Either in standalone versions, or incorporated in cell phones, the quality of the images has risen at a fast pace while their price has dropped drastically. Such pervasiveness has given rise to many unforeseen application such as using portable cameras for digitalizing documents by user of many different professional areas[1]. One of the major problem faced while acquiring document is illumination . Therefore, there is a need to elliminate such distortions and increase data quality for the camera captured documents which often suffers from distortions like uneven lighting, low resolution, motion blur and Perspective distortion which will greatly affect the performance which results in low recognition rate. Portable digital cameras were developed for taking picture of joy and sad moments but the recent price-performance improvement, low weight ,portability ,low cost ,small dimension has given birth to several new application[2][3]. This paper gives a detailed theoretical background on homomorphic filtering, along with some proposed modifications, specifically designed to address diffrent methods to reduce illunination and increase data quality for camera captured documents. We will show that by dividing the image 978-0-7695-4437-3/11 $26.00 © 2011 IEEE DOI 10.1109/CSNT.2011.79

Proposed Block diagram

A. Camera-based acquisition The advantage of using a camera in place of scanner is that, With help of digital camera we can capture characters and documents anywhere in the 3D environment without contact with the object like signs and billboards ,color, texture, shape, as well as the relationship between them. It is very difficult to project uniform lighting onto a document surface, and this often results in uneven illumination and color shift in the acquired images. The captured objects often suffers from distortions and it involves more preprocessing steps to be done in order to extract the text. This advantage of capturing has 350

helped to develop number of application and also more number of new problems increased. II. P ROPOSED M ETHODOLOGY The proposed method has three stages as described below. A. Pre-processing

Fig. 2.

The key function of the pre-processing is to improve the image in ways that increases the chances for success of other process. It ultimately deals with enhancement of image. Camera captured images suffer from noise due to low brightness contrast and various illuminated environment ,low resolution and broken characters are processed to extract text in document. We employ some basic enhancement techniques and homomorphic filtering to enhance image in our preprocessing step. A complete detail description of Homomorphic filtering is explained in next section.

Where Fi(u,v) and Fr(u,v) are the fourier transform of the illumination and reflectance component. Then,the desired filter function H(k,l) can be applied separately to the Z(u, v)∗H(k, l) = Fi (u, v)∗H(k, l)+Fr (u, v)∗H(k, l) (4) inverse fourier transform f −1 (n1 , n2 ) = = [Fi (u, v) ∗ H(k, l)] +=−1 [Fr (u, v) ∗ H(k, l)] (5) −1 g(n1 , n2 ) = ef (n1 ,n2 ) (6)

B. Homomorphic Filtering-Illumination-Reflactance Model

The fourier transformed signal is processed by means of a filter function H(u,v) and the resulting function is inverse fourier transformed. Finally, inverse exponential operation yields an enhanced image. This enhancement approach is termed as homomorphic filtering. The whole operation is expressed as a block diagram in figure 2.

Homomorphic filtering is a generalized technique for image enhancement[8]. It simultaneously normalizes the brightness across an image and increases contrast. Here homomorphic filtering is used to remove multiplicative noise. Illumination and reflectance are not separable, but their approximate locations in the frequency domain may be located. Since illumination and reflectance combine multiplicatively, the components are made additive by taking the logarithm of the image intensity, so that these multiplicative components of the image can be separated linearly in the frequency domain. Illumination variations can be thought of as a multiplicative noise, and can be reduced by filtering in the log domain. To make the illumination of an image more even, the high-frequency components are increased and low-frequency components are decreased, because the high-frequency components are assumed to represent mostly the reflectance in the scene, whereas the low-frequency components are assumed to represent mostly the illumination in the scene. That is, high-pass filtering is used to suppress low frequencies and amplify high frequencies, in the log-intensity domain An image as a function can be expressed as the product of illumination and reflectance components as follows: F (x, y, z) = i(x1 , y1 , z) ∗ r(x, y, z)

III. P ROPOSED M ODIFICATION We tested a standard homomorphic filtering over the whole image captured through Mobile Camera the result were very low. We then wanted to see if the results could be further improved. Many well-known enhancement algorithms such as histogram equalization, Morphological Operations and DoG methods were implemented on the image but obtained results were not up to satisfactory. In later stages we tried to split the original image into sub-images and filter each sub-image individually. First we decided to try and split the image into two halves vertically (thus obtaining two sub-images of the original image) and then apply the filter to each half individually. Second idea was to split the image horizontally and again apply the filter to each half individually. Encouraged by the good results obtained with both these methods. We further tried to combine the filtering results into a joint representation. Let IH F V (x, y) be the image split vertically and each half filtered with homomorphic filter individually, let IH F H (x, y) be the same for horizontally split images and let IM H F (x, y) be our proposed modification:

(1)

Equation (1) cannot be used directly to operate separately on the frequency components of illumination and reflectance because the Fourier transform of the product of two functions is not separable. Instead the function can be represented as a logarithmic function wherein the product of the Fourier transform can be represented as the sum of the illumination and reflectance components as shown below: ln(x, y) = ln(I(x, y)) + ln(R(x, y))

1 [0.25 ∗ HF V (x, y) + 0.5 ∗ IH F H (x, y)] 2 (7) By repetative iteration we found out that a constant value of 0.25 to be multiplied for Vertical homomorphic filtering and 0.5 constant for Horizontal homomorphic filtering ,This combination produced highest results in our experiments and was kept as a final representation. The whole procedure is summarized in Fig. 3.In the following section we can see that IM H F (x, y) =

(2)

The Fourier transform of equation (2) is Z(u, v) = F i(u, v) + F r(u, v)

Block Diagram of Homomorphic filtering

(3)

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the more characteristic features of a image are described in the particular eigenvector. Using the k eigenvectors Uk feature extraction done by PCA is follows: ¯ Fi = UkT (Ai − A)

Fig. 3.

(10)

The first principal component is the linear combination of the original dimensions that has the maximum variance: the nth principal component is the linear combination with the highest variance, subject to being orthogonal to the n-1 first principal components. The basic idea is to correspond to the direction of maximum variance and is chosen as the first principal component. With this we get the best one dimensional representation of the component images with reduced feature size. Principal component analysis (PCA) yields projection directions that maximize the total scatter across all classes. In choosing the projection which maximizes total scatter, PCA retains not only between-class scatter that is useful for classification, but also within-class scatter that is unwanted information for classification purposes.

Proposed Modification (Sub Image Homomorphic filtering)

our method yields satisfactory results,and therefore justifies further research of the homomorphic filtering variations as a means of simple yet efficient image preprocessing. IV. F EATURE E XTRACTION Feature extraction is the problem of identifying relevant information from raw data that characterize the component images distinctly. There are many popular methods to extract features. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. A survey of feature extraction schemes for character recognition is available in [4][5]. Different feature extraction methods are designed for different representations of the characters. Some of these features consider profiles, structural descriptors and transform domain representations [6]. Therefore we need to transform the features to obtain a lower dimensional representation. There are various methods employed in pattern recognition literatures for reducing the dimension of feature vectors [9]. In our present work we consider Principal Component Analysis (PCA)

V. C LASSIFICATION USING S UPPORT V ECTOR M ACHINE (SVM) Training and testing are the two basic phases of any pattern classification problem[12]. During training phase, the classifier learns the association between samples and their labels from labeled samples. We use Support Vector Machine (SVM) which has proved to be very successful in the field of Pattern Recognition and Machine Learning. SVM is based on the principal of structural risk minimization. Consider a Texture data points of the form {(x1 , y1 ), (x2 , y2 ), . . . , (xm , ym )} where x ∈