Edge Enhancement Method for Detection of Text From Handwritten Documents

2021 ◽  
pp. 207-215
Author(s):  
Chandrakala H. T. ◽  
Thippeswamy G.
Keyword(s):  
Edge Detection ◽  
Text Documents ◽  
Handwritten Documents ◽  
Handwritten Text ◽  
Detection Approach ◽  
Contrast Factor ◽  
Enhancement Method ◽  
Kannada Language ◽  
Sobel Edge Detection ◽  
Edge Detection Method

Edge detection from handwritten text documents, particularly of Kannada language, is a challenging task. Kannada has a huge character set, amounting to 17,340 character combinations. Moreover, in handwritten Kannada, the character strokes are highly variable in size and shape due to varying handwriting styles. This chapter presents a solution for edge detection of Kannada handwritten documents. Sobel edge detection method, which efficiently enhances the image contrast and detects the character edges, is proposed. Experimentation of this edge detection approach yielded high F-measure and global contrast factor values.

scholarly journals Unconstrained Handwritten Text Line Segmentation for Kannada Language

2019 ◽  
Vol 8 (12) ◽  
pp. 953-956
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Text Line ◽  
Connected Component ◽  
Horizontal Projection ◽  
Text Documents ◽  
Handwritten Text ◽  
Kannada Language ◽  
System Separation ◽  
Line Segmentation

Segmentation is division of something into smaller parts and one of the Component of character recognition system. Separation of characters, words and lines are done in Segmentation from text documents. character recognition is a process which allows computers to recognize written or printed characters such as numbers or letters and to change them into a form that the computer can use. the accuracy of OCR system is done by taking the output of an OCR run for an image and comparing it to the original version of the same text. The main aim of this paper is to find out the various text line segmentations are Projection profiles, Weighted Bucket Method. Proposed method is horizontal projection profile and connected component method on Handwritten Kannada language. These methods are used for experimentation and finally comparing their accuracy and results.

Using Local Edge Pattern Descriptors for Edge Detection

2017 ◽  
Vol 32 (03) ◽  
pp. 1850006 ◽  
Author(s):  
Yu Wang ◽  
Na Zhang ◽  
Huaixin Yan ◽  
Min Zuo ◽  
Cuiling Liu
Keyword(s):  
Edge Detection ◽  
Vital Role ◽  
Detection Methods ◽  
Weighted Fusion ◽  
Edge Strength ◽  
Detection Approach ◽  
Segmentation Pattern ◽  
Edge Detection Method ◽  
Local Edge ◽  
Edge Pattern

Edge detection is an active and critical topic in the field of image processing, and plays a vital role for some important applications such as image segmentation, pattern classification, object tracking, etc. In this paper, an edge detection approach is proposed using local edge pattern descriptor which possesses multiscale and multiresolution property, and is named varied local edge pattern (VLEP) descriptor. This method contains the following steps: firstly, Gaussian filter is used to smooth the original image. Secondly, the edge strength values, which are used to calculate the edge gradient values and can be obtained by one or more groups of VLEPs. Then, weighted fusion idea is considered when multiple groups of VLEP descriptors are used. Finally, the appropriate threshold is set to perform binarization processing on the gradient version of the image. Experimental results show that the proposed edge detection method achieved better performance than other state-of-the-art edge detection methods.

An improved sobel edge detection method based on generalized type-2 fuzzy logic

2014 ◽  
Vol 20 (2) ◽  
pp. 773-784 ◽  
Author(s):  
Claudia I. Gonzalez ◽  
Patricia Melin ◽  
Juan R. Castro ◽  
Olivia Mendoza ◽  
Oscar Castillo
Keyword(s):  
Fuzzy Logic ◽  
Edge Detection ◽  
Detection Method ◽  
Sobel Edge Detection ◽  
Edge Detection Method ◽  
Generalized Type

A Novel Edge Detection Approach Based on Soft Morphological Operations

2012 ◽  
Vol 220-223 ◽  
pp. 2828-2832
Author(s):  
Bo Chen ◽  
Meng Jia
Keyword(s):  
Edge Detection ◽  
Edge Effect ◽  
Detection Method ◽  
Experimental Results ◽  
Morphological Operations ◽  
The Novel ◽  
Target Segmentation ◽  
Detection Approach ◽  
Edge Detection Method ◽  
Image Details

Edge detection and target segmentation is difficult due to noise existing in an image. A novel edge detection method is proposed based on soft morphological operations in this paper. Because soft morphological operations can remove noise while preserving image details, which can be used to construct morphological edge detection operators with high robustness and better edge effect. Experimental results show that, comparing with the existing edge detection operators, the novel edge detection method can get better edge effect while removing pseudo edges.

An Edge Detection Approach for Fractal Image Processing

2020 ◽  
pp. 1-22
Author(s):  
Kalyan Kumar Jena ◽  
Sasmita Mishra ◽  
Sarojananda Mishra
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Method ◽  
Challenging Problem ◽  
Gray Scale ◽  
Fractal Image ◽  
Detection Approach ◽  
Fractal Images ◽  
Morphological Operator ◽  
Edge Detection Method

Research in the field of fractal image processing (FIP) has increased in the current era. Edge detection of fractal images can be considered as an important domain of research in FIP. Detecting edges in different fractal images accurate manner is a challenging problem in FIP. Several methods have introduced by different researchers to detect the edges of images. However, no method works suitably under all conditions. In this chapter, an edge detection method is proposed to detect the edges of gray scale and color fractal images. This method focuses on the quantitative combination of Canny, LoG, and Sobel (CLS) edge detection operators. The output of the proposed method is produced using matrix laboratory (MATLAB) R2015b and compared with the edge detection operators such as Sobel, Prewitt, Roberts, LoG, Canny, and mathematical morphological operator. The experimental outputs show that the proposed method performs better as compared to other traditional methods.

Recognition of Traffic Weight Using Sobel Edge Detection Method and K-Nearest Neighbor Algorithm

10.17158/220 ◽  
2012 ◽  
Vol 18 (1) ◽  
Author(s):  
Eric John G. Emberda ◽  
Lovie Mae N. Dalagan ◽  
Christy Faith O. Baguio
Keyword(s):  
Edge Detection ◽  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Software Application ◽  
K Nearest Neighbor Algorithm ◽  
Sobel Edge Detection ◽  
Edge Detection Method ◽  
The Given

This study explored the use of Sobel Edge Detection and K-Nearest Neighbor algorithm in classifying the traffic weight of a given captured image. A software application was created that accepts as input, a snapshot of a given intersection. The application could determine the traffic weight of the given snapshot, as whether it is light, moderate, or heavy by comparing it to a database of images using the K-Nearest Neighbor algorithm. The accuracy of the result was highly dependent on the training data and the quality of the snapshot. Overall, the use of Sobel Edge Detection and K-Nearest Neighbor algorithm gave significant results in recognizing the weight of a given snapshot of traffic.

scholarly journals Penerapan Metode Sobel Edge Detection dan Image Processing Untuk Mengetahui Diameter Apel Fuji Menggunakan Aplikasi Matlab

2020 ◽  
Vol 7 (3) ◽  
pp. 450
Author(s):  
Alima Hakkon Hasibuan ◽  
Taronisokhi Zebua ◽  
Rivalri Kristianto Hondro
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Method ◽  
Selling Price ◽  
Image Processing Techniques ◽  
Sobel Edge Detection ◽  
Yellow Line ◽  
Edge Detection Method ◽  
Processing Techniques

Fuji apples (Malus Domestika) are fruits that contain a lot of antioxidants. Besides the fruit flesh, the apple also contains pectin. Fuji apples are red and have a yellow line. The size of apples can affect the selling price of apples, the determination of the size of apples can be seen from the size of the diameter, measuring the diameter of apples is usually done visually by comparing apples. Based on these problems, a research is needed to develop a system to determine the diameter of fuji apples by using image processing techniques to find the diameter. This measurement process uses the matlab application and tests with the sobel edge detection method and image processing to see the more visible edges of the lines. The results showed that the developed system was able to obtain images and identify the diameter of fuji apples

Detection of Diabetic Retinopathy Using Sobel edge detection method in DIP

2012 ◽  
Vol 3 (7) ◽  
pp. 1-4
Author(s):  
Jyoti Patil ◽  
Dr. A. L. Chaudhari
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Edge Detection ◽  
Automated Analysis ◽  
Detection Algorithm ◽  
Sobel Edge Detection ◽  
Edge Detection Method ◽  
Image Edges ◽  
Processing Techniques ◽  
Automated Image Processing

Diabetic retinopathy, a complication of diabetes that occurs as a result of vascular changes in the retina, It is a major cause of loss of vision. Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel patterns in the retina. Image processing techniques can reduce the work of ophthalmologists and the tools used automatically locate the exudates. 0In this paper the process and knowledge of Digital Image Processing (DIP) is used. Automated analysis techniques for retinal images have been an important area of research for developing screening programmers. By using MATLAB for programming to develop the DIP tool for diagnosis of eye infection . Sobel edge detection algorithm is a method to find the edge pixels in an image. Edges are pixels which carry important information in an image. Thus sobel method is best technique for features are extended & used to classify the pixels in the patch into vessel and non vessel

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

2021 ◽  
Vol 5 (6) ◽  
pp. 1062-1069
Author(s):  
Shoffan Saifullah ◽  
Andiko Putro Suryotomo ◽  
Yuhefizar
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Detection Methods ◽  
Adaptive Histogram Equalization ◽  
Chicken Egg ◽  
Enhancement Method ◽  
Edge Detection Method

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values ​​of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

scholarly journals A NOVEL APPROACH FOR EDGE DETECTION OF LOW CONTRAST SATELLITE IMAGES

2015 ◽  
Vol XL-3/W2 ◽  
pp. 211-217 ◽  
Author(s):  
K. K. Singh ◽  
M. K. Bajpai ◽  
R. K. Pandey
Keyword(s):  
Edge Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Low Contrast ◽  
Quadrature Mirror Filter ◽  
Novel Approach ◽  
Low Pass ◽  
Sobel Edge Detection ◽  
Edge Detection Method ◽  
Low Pass Filters

Discrimination between texture edges and geometrical edges is very difficult in low contrast images. Satellite images are low contrast images. It is important to extract the edges that are not clearly visible in case of Satellite images. The present work encompasses a new edge detection algorithm using newly constructed differentiator. Chebyshev polynomial based fractional order differentiator has been used for filtering operation on an image. High pass and Low pass filters are designed with the concept of Quadrature Mirror Filter (QMF). Pre-processing has been performed by using this filter. Sobel edge detection method has been applied on this pre-processed image. The algorithm has been tested with two different satellite images.

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