Research Article International Journals of Advanced Research in Computer Science and Software Engineering ISSN: X (Volume-7, Issue-7)

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1 International Journals of Advanced Research in Computer Science and Software Engineering ISSN: X (Volume-7, Issue-7) Research Article July 2017 Technique for Text Region Detection in Image Processing Shivani, Dipti Bansal Department of Electrical Communication Engineering, Punjabi University, Patiala, Punjab, India DOI: /ijarcsse/V7I7/0150 Abstract- The image processing is the technique which is applied to process the digital information stored in the image. The OCR is the technique of image processing which will access the optical character information in the image. In the base paper, the technique is applied which will detect the text portion from the input image. The twp step are applied to detect the text portion, in the first back ground information is extracted and in the second step, technique of deep learning is applied which will mark the text region. In the work, SIFT algorithm is applied which will mark the key points for the key description. In the second step, technique of deep learning is applied which will mark the text area in the input image. The and algorithms are implemented in MATLAB and it is been analyzed that accuracy of algorithm is more as compared to algorithm. Keywords- Segmentation, Neural Networks, Edge Detection, SIFT. I. INTRODUCTION Image processing is referred to processing of a 2D picture by a computer. It is a form of signal privilege in which image is input similar to video frame or photograph and is image or characteristics associated with that image may be output. Segmentation is the process which done before the recognition process because it change the object into digital format and then divide it into the proper segment so that it is easy for the recognition to this object [1]. Since the different clusters are obtained in previous step, it is easy to segment the text-line by assigning the different colors for different clusters. The coloring on clusters is done by assigning the various RGB intensity values [2]. Text line segmentation is the method in which the image of the text is separate into units of patterns that seems to form a character. The accuracy of the entire recognition algorithm highly depends on the accuracy of the segmentation algorithm to break the image of text into individual characters. In edge based method the focus is on the high contrast between the text and the background. The edges of boundary of text are identified and merged and then several heuristics are used to filter out non text regions [3]. Edge filter is used to detect the edge and smoothing operation or morphological operator is used for the merging stage.texts in images have distinct textural properties that distinguish them from the background. Texture based methods are based on the textural properties of text. Gaussian filtering, Fourier transform, Discrete Cosine Transform are the mostly used texture analysis properties by these methods. In these methods features are extracted from a region and a classifier which is trained by heuristic or machine learning techniques is responsible for identifying the existence of text [4].To achieve number of tasks such as reduction of noise and re-sampling basic function of image processing is applied known as filtering. In the entire image processing, filtering is used as a basic process. The behavior of data and task performed by the each filter is determined by the filtering. By preserving important and useful information, filtering is used to remove noise of the image. The mean filter is a type of simple spatial filter. It is a sliding-window filter [5]. It replaces the center value in the window. It also replaces with the average mean of all the pixel values in the kernel or window. The window is usually square but it can be of any shape. Median Filter which is based on order statistics is a simple and powerful nonlinear filter. It is type of soothing image. Median filter is used for reducing the amount of intensity variation between one pixel and the other pixel [6]. In this filter, value is not replacing the pixel value of image with respect to neighbor pixel but it is replaced with the median value. II. LITERATURE REVIEW Prof. N.N. Khalsa1 et al, (2015) presented in this paper [7], that there are chances that the factors such as size, orientation, the similar patterns and other parameters can cause a problem in text detection. For such problems the text detection method has proved to be providing efficient results. Various steps are undertaken to detect the text. The variations such as geometric of text, its motion and edge, and also the color are helpful factors. The associated methods are identified with relevant to the problems coming. Such method helps in detecting text. The problems need to be held with the parameters that are problematic. The correct results are then available. All Rights Reserved Page 68

2 Tong He, et al, (2015) discussed in this paper [8] that late profound learning models have shown solid capacities for arranging text and non-text segments in natural images. In this work, there is another display of framework for scene text recognition by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that especially concentrates on extricating text-related areas and elements from the picture parts. Here there is a building up of another learning system to prepare the Text-CNN with multi-level and rich directed data, including text area veil, character name, and double text/non-text data. The enhancement permits it to distinguish profoundly difficult text designs, bringing about a higher review. Shashi Kant et al, (2015) presented in this paper [9] that the extraction and detection of text are very important and so their efficiency is a much prominent task. Its complexity arises with the task of determining the originity of the image. This article highlights a segmentation based text recognition method. It recognizes tests with a lot of variations much faster. End-to-end text recognition and segmentations have been performed on such texts to identify the images. Not only the efficiency results, but also there is an advantage of saving time, as it provides much less computational time for the user. So with this advantage it is useful to carry on with such method. M. Prabaharan et al, (2015) presented in this paper [10] that it is not an easy task to extract the text information from the image especially when there is a difference in the size, text, orientation and alignment. All such features result in making it a tough task to identify the text characters available. The images which have such complex information are much likely to have wrong results when processed. There are two methods which are followed in the new technique here. One is the adjacent character grouping and the other is the character stroke orientation method. The output results have been much more prominent through this system and the text extraction has proven to be easy through the availability of such technique. Mona Saudagar et al, (2014) presented in this paper [11] that the text image should be such that it can be easily understood by the common beings. This article gives a special method of identifying the image even though it contains different fonts and much more multi-oriented text. This problem still continues because there is no limit of complexity in the natural images available. The similar problems are grouped and only then they are solved. They are easy to be solved when groped and solutions are provided easily. The differently recognized problem is not much easy to solve due to its unrecognized solutions. The study is made much more extending. But the information with similar data is helpful and can be easily made correct. Chucai Yi, et al, (2014) presented in this paper [12] that content characters and strings in natural scene can give profitable data to numerous applications. Removing content straightforwardly from common scene pictures or recordings is a testing assignment as a result of different content examples and variation foundation obstructions. This paper proposes a technique for scene content acknowledgment from distinguished content areas. An Android-based demo framework is produced to demonstrate the adequacy of the technique on scene content data extraction from close-by items. The demo framework likewise gives some knowledge into calculation outline and execution change of scene content extraction. The assessment results on benchmark information sets show that the plan of content acknowledgment is equivalent with the best strategies. III. PROPOSED WORK The part-based tree structure model was designed in this work. This model was used to detect the text characters which are done through Latent-SVM. The identification of text words from the regions is done under a conditional random field. Feature matching was done through the Scale Invariant Feature Transform (SIFT). The false positive matches were removed by voting and geometric verifications. For general, the object recognition method was used to identify the scene text information. A dictionary is built up which is used to improve the accuracy of detection and identification. Using HOG features the character structure was modeled. The first stage is to construct a scale space. This is done by repeatedly convolving the input of the image with Gaussian Convolution Kernel. This produces a fixed series which consists of increasingly-blurred versions of the image. The Difference of Gaussian between adjacent images is calculated. Further the step involves locating the extrema keypoints which is done by comparing the Difference of Guassian values. Then the filtering of potentially unstable keypoints which are mainly in low contrast edges is done. After gathering the stable keypoints, the Gradient orientation histogram is calculated in the neighborhood of the keypoint. This is done for each blurred image for deciding the keypoint orientation. After this one can finally generate keypoint descriptors by using histogram calculations which hold the description of the neighbourhood of point. These histograms are clustered to form a vector which is used as a descriptor of keypoint which reflects the features of original image. The image is taken for implementation and keypoints are calculated. A text file is produces that has one line per keypoint with x and y coordinates, the scale, the orientation and 128 numbers representing the descriptor. After extraction of the features they can be matched using several distinct images by comparing them. The tasks are performed by computing geometric transformations between images to find overlapping areas. All Rights Reserved Page 69

3 START Input the natural scene images for the text area detection Improve the contrast of the input image to high light the text area Mark the character descriptors for the text area detection from the image Generated keypoints descriptors No Yes Assign keypoints orientation Built key point descriptors Detect the text area from the natural scene image based on key descriptors STOP Fig 1. Proposed Flowchart IV. EXPERIMENTAL RESULTS PSNR Comparison Fig. 2 PSNR Comparison All Rights Reserved Page 70

4 As shown in the figure 2, the PSNR values of the and algorithm are compared in terms of PSNR. It is been analyzed that PSNR value of the algorithm is high as compared to algorithm. MSE Comparison Fig. 3 MSE Comparison As shown in the figure 3, the MSE values of the and algorithm are compared. It is been analyzed that MSE value of the algorithm is less as compared to algorithm. Accuracy Comparison Fig. 4 Accuracy Comparison As shown in the figure 4, the accuracy of the and algorithm is compared in terms of accuracy. The algorithm shows that accuracy of algorithm is more as compared to algorithm. Recall Comparison Fig. 5 Recall Comparison As shown in figure 5, the performance of algorithm and algorithm is compared in terms of recall. It is been analyzed that Recall value of is less as compared to algorithm. All Rights Reserved Page 71

5 Ratio of f-measure Precision Comparison Fig 6: Precision Comparison As shown in figure 6, the precision value of algorithm is compared with algorithm. It is been analyzed that precision value of algorithm is less as compared to algorithm. F-measure Existing Proposed Fig. 7 F-measure Comparison As shown in figure 7,the F-measure of and is compared, It is been analyzed that F-measure of algorithm is more as compared to algorithm. V. CONCLUSION A technique namely Scale Invariant Feature Transform (SIFT) technique is used for the extraction and detection purposes. In the earlier technique MRSE algorithm is used for the text extraction from the natural scene images. It is been analyzed that some of the texts remain unrecognized due to wrong orientation of the image. In this works, improvement will be in MRSE technique using SIFT algorithm. This method directly leads to increase in accuracy of the detection, fault detection rate as well as the PSNR value of the image. ACKNOWLEDGMENT We thanks the reviewers for comments and suggesting and additional references. We also thanks to ECE department faculties to giving some useful suggestions. Finally we thanks all those authors who made their technical reports and publications readily available on the internet and the world wide web. REFERENCES [1] Chong yu, y. S., "Text detection and recoginition in natural scene with edge analysis", IET Computer Vision, 9 (4), 2015 [2] Lei Sun, Q. H, "A Robust Approach for Text Detection from Natural Scene", Pattern Recognition, , All Rights Reserved Page 72

6 [3] Honggang Zhang, Kaili Zhao, Yi-Zhe Song, Jun Guo, Text extraction from natural scene image: A survey, 2013 NEUCOM13479 [4] Huizhong Chen, S. S., "Robust Text Detection in Natural images With Edge Enhanced maximally Stable Extremal regions", 18th IEEE International Conference (pp ), Image Processing (ICIP), 2011 [5] Lukas Neumann, J. M, "Real-Time Scene Text Localization and Recognition", IEEE Conference (pp ). Computer Vision and Pattern Recognition (CVPR), 2012 [6] Xu-Cheng Yin, Xuwang Yin, Kaizhu Huang, and Hong-Wei Hao, Robust Text Detection in Natural Scene Images, IEEE Conference, 2013 [7] Prof. N.N. Khalsa1, Prof. S.G. Kavitkar, Nagendra.G.Kushwaha, A Literature Review on Variation in Text and Different methods for Text Detection in Images and Videos, IJIRCCE,2015 [8] Tong He, Weilin Huang, Yu Qiao, and Jian Yao, Text-Attentional Convolutional Neural Network for Scene Text Detection, IEEE Transcations on image Processing,2015 [9] Shashi Kant, Sini Shibu, Segmentation Framework for Multi-Oriented Text Detection and Recognition, 2015 IJEDR [10] M. Prabaharan1, K. Radha, Text Extraction from Natural Scene Images and Conversion to Audio in Smart Phone Applications, IJIRCCE,2015 [11] Mona Saudagar1, S. V. Jain, A study of multi-oriented text recognition in natural scene images, IJARCCE,2014 [12] Chucai Yi, and Yingli Tian, Scene Text Recognition in Mobile Applications by Character Descriptor and Structure Configuration, IEEE Transactions on Image Processing, Vol. 23, No. 27,2014 [13] Shivani and Dipti Bansal Techniques of Text Detection and Recognition: A Survey, IJERMT,2017,vol 6,no.6 All Rights Reserved Page 73