Open AccessNatural scene text detection based on YOLO V2 network model
Author(s) -
Haifeng Dong,
Han Siqi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1634/1/012013
Subject(s) - computer science , artificial intelligence , object detection , bounding overwatch , computer vision , image (mathematics) , text detection , minimum bounding box , pattern recognition (psychology) , field (mathematics) , translation (biology) , mathematics , biochemistry , chemistry , messenger rna , pure mathematics , gene
As an important part in the field, the natural scene text detection has been widely applied in visual navigation system, content-based image and video retrieval, instant translation system and so on. In this paper, we introduce several object detection network based on deep learning, and apply the YOLO v2 into natural scene text detection, changing the multi objects detection problems into the two classification problems. The main works in the paper include the following: prepare the datasets; we train the YOLO v2 with the optimum parameters, carry out the regression analysis of the coordinate parameters and categories of bounding boxes, obtain the detection result; according to different detection models, the detection results of different datasets are compared and analyzed, YOLO V2 model detection speed 0.105s/image has certain advantages.