计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 243-248.doi: 10.11896/jsjkx.201000154
陈卓, 王国胤, 刘群
CHEN Zhuo, WANG Guo-yin, LIU Qun
摘要： 自然场景下的文本信息通常具有多样性和复杂性的特点。由于采用手工设计特征的方式,传统的自然场景文字检测方法缺乏鲁棒性,而已有的基于深度学习的文本检测方法在各层网络提取特征的过程中存在丢失重要特征信息的问题。文中从多粒度和认知学的角度,提出了一种结合多粒度特征融合的自然场景文本检测方法。该方法的主要贡献是通过对通用特征提取网络的不同粒度特征进行融合,并加入残差通道注意力机制,使得模型在充分学习图像中不同粒度特征信息的基础上,更加关注目标特征信息并抑制无用的信息,提升了模型的鲁棒性和准确率。实验结果表明,相比其他最新的方法,该方法在公开数据集上取得了85.3%的准确率和82.53%的F值,具有更好的性能。
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