Huang, W., Lin, Z., Yang, J. and Wang, J. (2013) Text Localization in Natural Images Using Stroke Feature Transform and Text Covariance Descriptors. IEEE International Conference on Computer Vision, Sydney, 1-8 December 2013, 1241-1248.
https://doi.org/10.1109/ICCV.2013.157

has been cited by the following article:

• TITLE: An Oracle Bone Inscription Detector Based on Multi-Scale Gaussian Kernels

  AUTHORS: Guoying Liu, Shuanghao Chen, Jing Xiong, Qingju Jiao

  KEYWORDS: Oracle Bone Inscriptions, Deep Learning, Object Detection, Hourglass Network

  JOURNAL NAME: Applied Mathematics, Vol.12 No.3, March 30, 2021

  ABSTRACT: The detection of Oracle Bone Inscriptions (OBIs) is one of the most fundamental tasks in the study of Oracle Bone, which aims to locate the positions of OBIs on rubbing images. The existing methods are based on the scheme of anchor boxes, involving complex network design and a great number of anchor boxes. In order to overcome the problem, this paper proposes a simpler but more effective OBIs detector by using an anchor-free scheme, where shape-adaptive Gaussian kernels are employed to represent the spatial regions of different OBIs. More specifically, to address the problem of misdetection caused by regional overlapping between some tightly distributed OBIs, the character regions are simultaneously represented by multiscale Gaussian kernels to obtain regions with sharp edges. Besides, based on the kernel predictions of different scales, a novel post-processing pipeline is used to obtain accurate predictions of bounding boxes. Experiments show that our OBIs detector has achieved significant results on the OBIs dataset, which greatly outperforms several mainstream object detectors in both speed and efficiency. Dataset is available at http://jgw.aynu.edu.cn.