Dense Text Detection Method in Natural Scene
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    Abstract:

    Text detection in natural scenes is one of the difficulties in the field of image processing. An efficient and accurate scene text detector (EAST) algorithm is an excellent text detection algorithm in recent years, but the AdvancedEAST algorithm after the addition of post processing still has the problem of missed detection caused by the loss of the head and tail boundaries of the activated pixels. Thus, the detection effect of dense texts is not ideal. For this reason, an improved algorithm of dilated-corner attention EAST (DCA_EAST) is proposed, and a dilated convolution module and a corner attention module are added to the network structure to improve the missed detection. For the loss function, weight factors of category and sample difficulty are introduced to effectively improve the detection effect of dense texts. The experimental results show that the proposed algorithm has an accuracy of 93.02%, a recall rate of 76.69%, and an F-measured value of 84.07% on the ReCTS dataset of ICDAR2019, thus being superior to the AdvancedEAST algorithm.

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牟森,陈洪刚,卿粼波,何小海,王思怡.自然场景下的密集文本检测方法.计算机系统应用,2021,30(2):171-175

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History
  • Received:June 18,2020
  • Revised:July 14,2020
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  • Online: January 29,2021
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