1 结合MSCRs与MSERs的自然场景文本检测方法.DOC

中图法分类号:TP391.4 文献标识码:A 文章编号:1006-8961(年) - 结合MSCRs与MSERs场景文本 易尧华 , 申春辉 , 刘菊华 , 卢利琼 武汉大学印刷与系武汉 430072 摘 要：目的：目前，基于MSERs(maximally stable extremal regions)的文本检测方法是自然场景图像文本检测的主流方法。但是自然场景图像中部分文本的背景复杂多变，MSERs算法无法将其准确提取出来，降低了该类方法的鲁棒性。本文针对自然场景多变的特点，MSCRs(maximally stable color regions)算法自然场景文本检测，提出MSCRs与MSERs的场景文本检测方法MSCRs算法与MSERs算法提取区域然后区域的特征训练森林字符分类器对候选区域进行得到字符区域区域一致性和几何邻接关系字符检测结果方法在 2013上的召回率、准确率和F值分别为%、%和77.5%，相对于其他的召回率和F有所提高实验结果。检测MSCRs；MSERs Natural scene text detection method by integrating MSCRs into MSERs Yi Yaohua, Shen Chunhui, Liu JuHua, Lu Liqiong School of Printing and Packaging, Wuhan University, Wuhan 430072 Abstract: Objective: Text detection methods based on the maximally stable extremal regions (MSERs) algorithm are now widely used in natural scene text detection. However, text regions in natural scene images can have complex backgrounds that differ from those in documents and business cards, which cannot be accurately extracted by the MSERs algorithm. A text detection method is proposed for natural scene images by integrating the maximally stable color regions (MSCRs) into MSERs in this study to overcome the said problem. Method: The character candidates are first extracted with both the MSCRs and MSERs algorithms. Parts of the non-character candidates are then eliminated according to the geometric information. The texture features are exploited to distinguish the character and non-character candidates, and a random forest character classifier is trained. The non-character candidates are then eliminated according to the classification result of the character classifier. Finally, the single character candidates are grouped into text regions according to the color similarity and geometric adjacency information. Result: The proposed natural scene text detection method achieved 71.9%, 84.1%, and 77.5% in recall rate, precision rate, and f-score on the ICDAR 2013 database, respectively. The recall rate and f-score improved, unlike other state-of-th