In this paper, we present a bottom-up approach for robust spotting of texts in scenes. In the proposed technique, character candidates are first detected using our proposed character detector, which leverages on the strengths of an Extremal Region (ER) detector and an Aggregate Channel Feature (ACF) detector for high character detection recall. The real characters are then identified by using a novel convolutional neural network (CNN) filter for high character detection precision. A hierarchical clustering algorithm is designed which combines multiple visual and geometrical features to group characters into word proposal regions for word recognition. The proposed technique has been evaluated on several scene text spotting datasets and experiments show superior spotting performance.

在本文中，我们提出了一种自底向上的方法，用于在场景中稳健地发现文本。在所提出的技术中，首先使用我们提出的字符检测器来检测候选字符，该字符检测器利用极值区域（ER）检测器和聚合通道特征（ACF）检测器的优势来实现高字符检测的查全率。然后，通过使用新颖的卷积神经网络（CNN）过滤器来识别真实字符，以实现较高的字符检测精度。设计了一种层次聚类算法，该算法结合了多个视觉和几何特征，将字符分组到用于单词识别的单词建议区域中。所提出的技术已经在多个场景文本点集数据集上进行了评估，并且实验显示了出色的点集性能。