Location of Scene Text Based on Yolov7
DOI:
https://doi.org/10.54097/fcis.v2i3.5509Keywords:
Text detection, YOLOv7, Deep learningAbstract
Text is everywhere in daily life, and it carries rich and accurate information. Natural scene text detection technology can be widely used in various fields of life. Chinese is an important tool to carry culture. Therefore, it is of great significance to study natural scene Chinese text detection. For text location in Chinese scene, we use target detection method to train based on YOLOv7 model, and obtain effective detection results.
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References
Judd T. Learning to predict where humans look IEEE international conference on computer vision[J]. Proc. ICCV, 2009, 2009.
Breuel T M, Yanikoglu B A, Berkner K. The OCRopus open source OCR system (Proceedings Paper) [J]. IEEE, 2002.
Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection[C]. IEEE Computer Society Conference on Computer Vision & Pattern Recognition, 2005.
Low D G. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision, 2004.
Ojala T, Pietikainen M, Maenpaa T. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns[C]. IEEE, 2002: 971-987.
Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.
Liu W, Berg A C, Fu C Y, et al. SSD: Single Shot MultiBox Detector[C]. ECCV, 2016: 21-37.
Liao M, Shi B, Bai X, et al. TextBoxes: a fast text detector with a single deep neural network[C]. National Conference on Artificial Intelligence, 2017.
Shi B, Bai X, Belongie S. Detecting oriented text in natural images by linking segments[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, 2017: 2550-2558.
Ma J, Shao W, Ye H, et al. Arbitrary-Oriented Scene Text Detection via Rotation Proposals[J]. IEEE Transactions on Multimedia, 2017: 3111-3122.
Wang C-Y, Bochkovskiy A, Liao H-Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J]. arXiv preprint arXiv:2207.02696, 2022.
Ding X, Zhang X, Ma N, et al. RepVGG: Making VGG-style ConvNets Great Again[C]. IEEE, 2021: 13728-13737.
