文章基本信息

标题：Text Detection and Recognition from Natural Scene
本地全文：下载
作者：Tajinder Kaur ; Nirvair Neeru
期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2015
卷号：4
期号：7
页码：3211-3216
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Many techniques and algorithm have been developed to solve the problem of text extracted from natural scenes. Text extraction is emerging and challenging era in the computer vision. Text which is embedded into the image contains semantic information which is used in many other applications such as information retrieval of complex images, robot navigation, useful for visually impaired persons, street signs, automatic read the sign board and use in so many other applications. Most of the research work in this area has been done only on printed text, a very few research is addressing the LED scene text. Scene text is difficult to extract due to blur image, variations in color, noise problem, complex background, discontinuity, poor lighting conditions, and variation in illumination. LED is which is widely used in displaying the information in boards. Now days LED display that is natural scene is being widely used for displaying announcements, sign boards, banners for displaying information. To extract the text from the LED display is not an easy task, it is very complex due to its discontinuity. So, The aim of this paper to propose a technique to extract the two type of LED text from natural scene image. The first step of the algorithm is preprocessing of the image where the image is converted from RGB to grayscale, noise is removed and the image is converted to binary image, etc. Then the text is localized. After that connected component approach is used for text detection and finally the text has been recognized using template matching with correlation. The experimental results of the proposed method show the detection and recognition rate is 82.87 and 57.6 .
关键词：Connected Component method; Corelation; Dilation; ; FFT method; Morphological operations; Template matching; Text ; detection and recognition