.png){kind=icon}
返回
Text analysis using local energy
.png){kind=icon}
PRE
AI
DOI：10.1016/S0031-3203(00)00155-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a biologically inspired texture-based algorithm using local energy analysis for (1) segmenting text embedded in clutter and (2) classifying text scripts without any explicit knowledge of the type of text present. The local energy model has been shown to work well in texture analysis, where texture segmentation and discrimination are preattentive tasks in human vision. The algorithm for text segmentation involves computing the local energy using a bank of orthogonal pairs of Gabor filters at various orientation and frequency. Similarly, local energy analysis is applied to the task of text script classification using a set of descriptors derived from the local energy information. The segmentation algorithm is quite successful in segmenting text of any arbitrary language script from real images with or without noise. It is also invariant to scale, rotation and position changes in text. The classification scheme was tested on 16 languages with the results obtained consistent with visual classification performed by humans. The scheme is insensitive to orientation of the text script. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keyword:
text analysis
local energy
Gabor filtering
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
.png){kind=icon}
查看更多内容