@inproceedings{0c6537f3b52542928dbf4d31060a17bb,
title = "Convolutional neural network for freight train information recognition",
abstract = "In this paper, the recognition of freight train's text information is studied. The text has the characteristics of discontinuous strokes, large interval between strokes and serious corrosion by environmental factors in this application scenario. The traditional template matching or geometric feature extraction cannot achieve a good recognition effect. Instead, the convolutional neural network (CNN) which is trained by a large number of image samples obtained by previous image segmentation is selected for recognition. The image segmentation processing use the Suzuki algorithm for the original image contour extraction to determine the text area. The result of contour extraction is projected horizontally using the edge information of the text area. The traversal template that combines with fixed aspect ratio of the text segmented the text image to a single text image.",
keywords = "Convolutional neural network (CNN), Image segmentation, Text recognition",
author = "Wenlong Zhang and Guanglu Zhou and Meiqi Jiang",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 9th International Conference on Machine Learning and Computing, ICMLC 2017 ; Conference date: 24-02-2017 Through 26-02-2017",
year = "2017",
month = feb,
day = "24",
doi = "10.1145/3055635.3056598",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery ",
pages = "167--171",
booktitle = "Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017",
address = "美国",
}