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Research on text detection on building surfaces in smart cities based on deep learning

  • Data analytics and machine learning
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Abstract

In recent years, with the construction and development of smart cities, text recognition in building images can not only achieve geolocation but also provide guiding significance for GIS mapping and automatic updating. Since buildings have different orientations, angles and shapes, it is difficult to recognize textual features in images. With the wide application of convolutional neural networks and recurrent neural networks in image processing, this paper proposes a BFPN-RCNN algorithm for detecting and recognizing curved text in architectural images. A comparison with other image detection algorithms on different datasets proves that the algorithm can effectively identify curved text at different angles in natural scene images.

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Funding

This work was supported by the Chongqing Natural Science Foundation of China (Grant No. cstc2021jcyj-bsh0218), the Chongqing Science and Technology Bureau of China (Grant No. D63012021013), The National Natural Science Foundation of China (Grant No. U21A20447 and 61971079), The Basic Research and Frontier Exploration Project of Chongqing (Grant No. cstc2019jcyjmsxmX0666), Chongqing technological innovation and application development project (Grant No.cstc2021jscx-gksbx0051), The Innovative Group Project of the National Natural Science Foundation of Chongqing (Grant No. cstc2020jcyj-cxttX0002), and the Regional Creative Cooperation Program of Sichuan (Grant No.2020YFQ0025) and The Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No.KJZD-k202000604).

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Authors and Affiliations

  1. School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Yuanpeng Long

  2. Southwest Medical University, Luzhou, 646000, China

    Guo Zhang

  3. Chongqing University of Posts and Telecommunication, Chongqing, 400065, China

    Weiwei Sun, Yu Pang, Huiqian Wang & Guo Zhang

Authors
  1. Yuanpeng Long
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  2. Weiwei Sun
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  3. Yu Pang
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  4. Huiqian Wang
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  5. Guo Zhang
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Contributions

Guo Zhang contributed to conceptualization; Guo Zhang and Yuanpeng Long contributed to methodology and guidance of the project; Yuanpeng Long and Guo Zhang contributed to validation, formal analysis and data analysis; Yuanpeng Long, Weiwei Sun, Yu Pang, Huiqian Wang and Guo Zhang contributed to writing.

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Correspondence to Guo Zhang.

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Long, Y., Sun, W., Pang, Y. et al. Research on text detection on building surfaces in smart cities based on deep learning. Soft Comput 26, 10103–10114 (2022). https://doi.org/10.1007/s00500-022-07391-3

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