TEXT DETECTION AND RECOGNITION IN CAMERA BASED IMAGES

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1 TEXT DETECTION AND RECOGNITION IN CAMERA BASED IMAGES Mr. Vishal A Kanjariya*, Mrs. Bhavika N Patel Lecturer, Computer Engineering Department, B & B Institute of Technology, Anand, Gujarat, India. ABSTRACT: Digital image processing is the use of computer algorithms to perform image processing on digital images. Image segmentation is an important and challenging process of image processing. Image segmentation technique is used to partition an image into meaningful parts having similar features and properties. The main aim of segmentation is simplification i.e. representing an image into meaningful and easily analyzable way. The goal of image segmentation is to divide an image into several parts/segments having similar features or attributes. The detection and extraction of text regions in an image is a well-known problem in the computer vision research area. The goal of this paper is to compare two basic approaches to text extraction in natural (non-document) images: edge-based and connected-component based. The algorithms are implemented and evaluated using a set of images of natural scenes that vary along the dimensions of lighting, scale and orientation. Accuracy, precision and recall rates for each approach are analyzed to determine the success and limitations of each approach. Recommendations for improvements are given based on the results. KEYWORDS: Image Segmentation, Edge Detection, Noise, Digital Image Processing, Image Identification INTRODUCTION Content-based image indexing refers to the process of attaching labels to images based on their content. Image content can be divided into two main categories: perceptual content and semantic content [1]. Perceptual content includes attributes such as color, intensity, shape, texture, and their temporal changes, whereas semantic content means objects, events, and their relations. A number of studies on the use of relatively low-level perceptual content [2-6] for image and video indexing have already been reported. Studies on semantic image content in the form of text, face, vehicle, and human action have also attracted some recent interest [4-8]. Among them, text within an image is of particular interest as (i) it is very useful for describing the contents of an image; (ii) it can be easily extracted compared to other semantic contents, and (iii) it enables applications such as keyword-based image search, automatic video logging, and text-based image indexing. A visual image is rich in information. The main purpose of image enhancement is to bring out detail that is hidden in an image or to increase contrast in a low contrast image. Image enhancement is among the simplest and most appealing areas of digital image Page 1

2 processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured or simply to highlight certain features of interest in an image. The detection and extraction of text regions in an image is a well-known problem in the computer vision research area. Compressing an image is significantly different than compressing raw binary data. General purpose compression programs can be used to compress images, but the result is less than optimal. This is because images have certain statistical properties which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image can be sacrificed for the sake of saving a little more bandwidth or storage space. This also means that lossy compression techniques can be used in this area. RELATED WORKS Text extraction in an image encompasses four phases, namely, (i) Detection, (ii) Localization (iii) Extraction and (iv) Recognition. The main objective of the two phases is to spot all text objects in a natural scene image and to provide a unique identity to each text. Due to the variety of font size, style, orientation, and alignment as well as the complexity of the background, designing a robust general algorithm, which can effectively detect and extract text from natural scene images, is a challenging task. Various methods have been proposed in the past for detection and localization of text in images and videos. These approaches take into consideration different properties related to text in an image such as color, intensity, region, connected components, texture, edges, etc. These properties are used to discriminate text regions from non-text regions within the image. In this section, the recent works focused on text detection, localization, line segmentation and character extraction are reviewed. The various methods proposed in these works can be categorized as region based,morphology based and texture based methods. [2] The algorithm proposed by Wang and Kangas in is based on color clustering. The input image is first pre-processed to remove any noise if present. Then the image is grouped into different color layers and a gray component. This approach utilizes the fact that usually the color data in text characters is different from the color data in the background. The potential text regions are localized using connected component based heuristics from these layers. Also an aligning and merging analysis (AMA) method is used in which each row and column value is analyzed. [3] This section discusses text tracking, extraction, and enhancement methods. In spite of its usefulness and importance for verification, enhancement, speedup, etc., tracking of text in video has not been studied extensively. There has not been much research devoted to the problems of text extraction and enhancement either. However, owing to inherent problems in locating text in images, such as low resolution and complex backgrounds, these topics need more investigation. [4] To enhance the system performance, it is necessary to consider temporal changes in a frame sequence. The text tracking stage can serve to verify the text localization results. In addition, if text tracking could be performed in a shorter time than text detection and localization, this would speed up the overall system. In cases where text is occluded in different frames, text tracking can help recover the original image. [5] Page 2

3 PROPOSED METHODLOGY HAAR DISCRETE WAVELET TRANSFORM The discrete wavelet transform is a very useful tool for signal analysis and image processing, especially in multi resolution representation. It can decompose signal into different components in the frequency domain. One-dimensional discrete wavelet transform (1-D DWT) decomposesaninput sequence into two components (the average component and the detail component) by calculations with a low-pass filter and a high-pass filter. [2] Two-dimensional discrete wavelet transform (2-D DWT) decomposes an input image into four sub-bands, one average component (LL) and three detail components (LH, HL, HH) as shown in Figure-1. In image processing, the multi-resolution of 2-D DWT has been employed to detect edges of an original image. The traditional edge detection filters can provide the similar result as well. However, 2-D DWT can detect three kinds of edges at a time while traditional edge detection filters cannot. Figure-1 show the grey level image Structural Segmentation Techniques: The structural techniques are those techniques of image segmentation that relies upon the information of the structure of required portion of the image i.e. the required region which is to be segmented. Stochastic Segmentation Techniques: The stochastic techniques are those techniques of the image segmentation that works on the discrete pixel values of the image instead of the structural information of region. Hybrid Techniques: The hybrid techniques are those techniques of the image segmentation that uses the concepts of both above techniques i.e. these uses discrete pixel and structural information together. [5] Page 3

4 Figure-2 Image Segmentation technique Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scène. Classical methods of edge detection involve convolving the image with an operator (a 2-D filter), which is constructed to be sensitive to large gradients in the image while returning values of zero in uniform regions. [1] Edge detection is difficult in noisy images, since both the noise and the edges contain high-frequency content. Attempts to reduce the noise result in blurred and distorted edges. Operators used on noisy images are typically larger in scope, so they can average enough data to discount localized noisy pixels. Not all edges involve a step change in intensity. Effects such as refraction or poor focus can result in objects with boundaries defined by a gradual change in intensity. [1] There are many ways to perform edge detection. However, the majority of different methods may be grouped into two categories: Gradient based Edge Detection: The gradient method detects the edges by looking for the maximum and minimum in the first derivative of the image. Laplacian based Edge Detection: The Palladian method searches for zero crossings in the second derivative of the image to find edges. An edgehas the one-dimensional shape of a ramp and calculating the derivative of the image can highlight its location. TEXT REGION EXTRACTION In this subsection, we use morphological operators and the logical AND operator to further removes the non-text regions. In text regions, vertical edges, horizontal edges and diagonal edges are mingled together while they are distributed separately in non-text regions. Since text regions are composed of vertical edges, horizontal edges and diagonal edges, we can determine the text regions to be the regions where those three Page 4

5 kinds of edges are intermixed. Text edges are generally short and connected with each other in different orientation. we use different morphological dilation operators to connect isolated candidate text edges in each detail component sub-band of the binary image. In this research, 3 5 for horizontal operators, 3 3 for diagonal operators and 7 3 for vertical operators. The dilation operators [9] for the three detail sub-bands are designed differently so as to fit the text characteristics. The logical AND is then carried on three kinds (vertical, horizontal and diagonal) of edges after morphological dilation. REMOVING NON-TEXT REGIONS To further enhance the results, the non-text regions are removed. The horizontal rectangular areas with high density indicate text strings. Projection is a more efficient way to find such high density areas. The idea of coarse-to-fine detection is to locate the text region progressively by two phase projection. There are lots of detected edge dense blocks that include multi-line texts. The projection profile is used to separate these blocks into single line text. A horizontal/vertical projection profile is defined as the vector of the sums of the pixel intensities over each column/row. [9] The horizontal and vertical projection of the processed edge map is found. The average of the minimum and maximum value of the vertical projection is taken as the threshold. Then the rows whose sums of pixel intensities are above the threshold are taken. Next, the horizontal projection of only those rows is found. The minimum and maximum of the horizontal projection is taken and the average of them is taken as the threshold. [10] Figure-3 Advantages and Disadvantages of Edge Detecting techniques Page 5

6 CONCLUSIONS A method of text extraction from images is proposed using the Haar Discrete Wavelet Transform, the Sobel edge detector, the weighted OR operator, thresolding and the morphological dilation operator. These mathematical tools are integrated to detect the text regions from the complicated images. The proposed method is robust against language and font size of the texts. The proposed method is also used to decompose the blocks including multi-line texts into single line text. According to the experimental results, the proposed method is proved to be efficient for extracting the text regions from the images.. REFERENCES 1. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 2nd ed., Beijing: Publishing House of Electronics Industry, SEGMENTATION OF TEXT FROM COMPOUND IMAGES by Dr.N.Krishnan, C. Nelson Kennedy Babu, S.Ravi and JosphineThavamani presented in International Conference on Computational Intelligence and Multimedia Applications Xiaoqing Liu and JagathSamarabandu, Multiscale edge-based Text extraction from Complex images, IEEE, Matlab version (R2010b) license no T. Shraddha, K. Krishna, B. K. Singh and R. P. Singh, Image Segmentation: A Review, International Journal of Computer Science and Management Research Vol. 1 Issue. 4 November M. R. Khokher, A. Ghafoor and A. M. Siddiqui, Image segmentation using multilevel graph cuts and graph development using fuzzy rule-based system, IET image processing, V. Dey, Y. Zhang and M. Zhong, a review on image segmentation techniques with Remote sensing perspective, ISPRS, Vienna, Austria, Vol. XXXVIII, July S. Inderpal and K. Dinesh, A Review on Different Image Segmentation Techniques, IJAR, Vol.. 4, April, D. Comaniciu, P. Meer, Mean shift: A robust approach toward feature space analysis, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002, 24, pp C. Christoudias, B. Georgescu, P. Meer, Synergism in Low Level Vision, Intl Conf on Pattern Recognition, 2002, 4, pp B. Georgescu, I. Shimshoni, P. Meer, Mean Shift Based Clustering in High Dimensions: A Texture Classification Example, Intl Conf on Computer Vision, P. Felzenszwalb, D. Huttenlocher, Efficient Graph-Based Image Segmentation, Intl Journal of Computer Vision, 2004, 59 (2) 13. E. Argyle. Techniques for edge detection, Proc. IEEE, vol. 59, pp , F. Bergholm. Edge focusing, in Proc. 8th Int. Conf. Pattern Recognition, Paris, France, pp , R. C. Gonzalez and R. E. Woods. Digital Image Processing. 2nd ed. Prentice Hall, Page 6

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