Feature based Text Extraction System using Connected Component Method

Nitigya Sambyal, P. Abrol
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引用次数: 15

Abstract

Text detection and segmentation system serves as important method for document analysis as it helps in many content based image analysis tasks. This research paper proposes a connected component technique for text extraction and character segmentation using maximally stable extremal regions MSERs for text line formation followed by connected components to determined separate characters. The system uses a cluster size of five which is selected by experimental evaluation for identifying characters. Sobel edge detector is used as it reduces the execution time but at the same time maintains quality of the results. The algorithm is tested along a set of JPEG, PNG and BMP images over varying features like font size, style, colour, background colour and text variation. Further the CPU time in execution of the algorithm with three different edge detectors namely prewitt, sobel and canny is observed. Text identification using MSER gave very good results whereas character segmentation gave on average 94.572% accuracy for the various test cases considered for this study.
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基于连通构件方法的特征文本提取系统
文本检测与分割系统作为一种重要的文档分析方法,在许多基于内容的图像分析任务中发挥着重要的作用。本文提出了一种连接组件技术用于文本提取和字符分割,该技术使用最稳定的极值区域mser进行文本行形成,然后使用连接组件确定单独的字符。该系统使用5个聚类大小,通过实验评价选择聚类大小来识别字符。使用索贝尔边缘检测器,因为它减少了执行时间,但同时保持了结果的质量。该算法在一组JPEG、PNG和BMP图像上进行了测试,测试了不同的特征,如字体大小、风格、颜色、背景颜色和文本变化。进一步观察了在prewitt、sobel和canny三种不同边缘检测器下算法执行时的CPU时间。使用MSER的文本识别给出了非常好的结果,而对于本研究考虑的各种测试用例,字符分割的平均准确率为94.572%。
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