A scene text detector based on deep feature merging

Yong Zhang, Yubei Huang, Donning Zhao, Chun Ho Wu, Wai Hung Ip, Kai Leung Yung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Scene text detection has become an important research topic. It can be broadly applied to much industrial equipment, such as smart phones, intelligent scanners, and IoT devices. Many existing scene text detection methods have achieved advanced performance. However, text in scene images is presented with differing orientations and varying shapes, rendering scene text detection a challenging task. This paper proposes a method for detecting texts in scene images. First, four stages of low-level features is extracted using DenseNet121. Low-level features are then merged by transposed convolution and skip connection. Second, the merged feature map is used to generate a score map, box map, and angle map. Finally, the Locality-Aware Non-Maximum Suppression (LANMS) is applied as post-processing to generate the final bounding box. The proposed method achieves an F-measure of 0.826 on ICDAR 2015 and 0.761 on MSRA-TD500, respectively.

Original languageEnglish
Pages (from-to)29005-29016
Number of pages12
JournalMultimedia Tools and Applications
Issue number19
Publication statusPublished - Jun 2021


  • Convolutional neural network
  • Deep feature merging
  • DenseNet121
  • Scene text detector

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


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