Text extraction from invariant complex image

Abstract

Great progress has been made in Optical Character Recognition (OCR) technology. Most current OCRs, however, can only read characters printed on sheets of paper according to some rigid format restrictions. For that, the detection and extraction of text regions in an image are well known problems in Computer Vision research area. The goal of this project is to extract and recognize the text from an image by using the edge-based and fuzzy logic algorithm respectively. The algorithms are implemented and evaluated by using a set of images of natural scenes that vary along its’ size, scale and orientation. Various kernels can be used for this operation ,the whole set of 8 kernels is produced by taking one of kernels and rotating its coefficient circularly and edgedetection operator is calculated by forming matrix centered on pixel chosen as center of matrix area, then Localization involves further enhancing regions by eliminating nontext regions. Edge-detection works quite well for digital image corrupted with multiscale and multi-orientation whereas its performance of this operator cannot be used in practical image which generally corrupted other types and edge-detection for detection of edge in digital image is that image should contain sharp intensity transition and low noise of the type is present. Moreover the image is colored image .Then, edge detect at eight edges and convolve with Gaussian after that select the strong edge was suitable of detect the text. As known be the project in complex image by using eight kernels to accomplish the task .Then, we used identified pixel of determine the character with use fuzzy logic.