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An image processing technique called edge detection is used to find and identify the edges, curves, and objects in digital images. Edge detection includes various mathematical calculations that focus on locating the edges and curves where a digital image's brightness abruptly shifts or has discontinuities. In the fields of feature detection and feature extraction in image processing and computer vision, edge detection is a fundamental tool. The application of an edge detector results in the development of a set of connected curves that represent surface marks, character boundaries, and discontinuities in surface orientation. Character boundaries are described via edges. The majority of a feature is described by its edges. A group of pixels called an edge is used to describe an area where abrupt variations in intensity occur. Because it removes items of interest for actions like description, segmentation is a vital stage in an image recognition system. This paper focuses on edge detection techniques that can be implemented on characters for effective character recognition. Devanagari text includes curvy edges which need to be detected for proper recognition hence different edge detector operators are implemented to find the most effective amongst them to acquire clear edges of the text. There are several methods for segmenting images, including segmentation based on artificial neural networks, clustering, edges, regions, and thresholds. In this paper, edge-based segmentation is used. The Canny, Laplacian, Sobel, Prewitt, and Robert operators are used to detect edges, and the generated image is compared to the original binary image. The pre-processing phase includes the binarization step. |
