基于双门限梯度模式的图像文字检测方法

对复杂自然背景下的图像文字检测技术进行了研究,提出了一种基于双门限梯度模式的图像文字检测方法。首先,在文字粗检测阶段中,该方法抽取了最大极值稳定区域(Maximally Stable Extremal Regions,MSER)作为候选文字区域,避免了对整幅图像进行扫描,极大地提高了检测速度和实时性；其次,在文字精检测阶段的特征提取部分,为了克服文字区域颜色对比反转问题和自然图像 的噪声干扰问题,提出了一种双门限梯度模式特征来描述文字区域的纹理特征；最后,在文字精检测的检测器设计中,利用极限学习机构造新的级联型ELM(Extreme Learning Machine)检测器,极大地缩短了分类器的训练时间。实验结果表明,该方法不仅具有优良的检测性能,而且能极大地缩短分类器训练时间和检测时间。

New Image Text Detection Method Based on Double-threshold Gradient Pattern

This paper studied the traditional image text detection approaches and proposed a new image text detection method based on double-threshold gradient pattern with a pretty fast speed in both classifier training and implementing.Firstly,in the rough detection phase,the maximally stable extremal regions(MSER) was extracted as a candidate text area,to avoid scanning the whole image,greatly improving the detection speed and real-time.Secondly,in the feature extraction part of refine detection phase,in order to overcome the text area color contrast inversion problem and the problem of noise in natural image,this paper creatively presented a dual threshold gradient mode feature to describe the texture of the text area feature.Finally,to design the detector for text fine detection,this paper designed a new Cascade ELM(Extreme Learning Machine) detector by limit learning machine,which greatly shortens the classifier training time.The experimental results show that this method not only has excellent detection performance,but also greatly shortens the classifier training time and testing time.