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Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster

Citation Author(s):
Junting Zhang, Yuewei Na, Siyang Li, C.-C. Jay Kuo
Submitted by:
Junting Zhang
Last updated:
12 April 2018 - 5:31pm
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Junting Zhang
Paper Code:
ADL-O.2.3
 

Scene text detection is a critical prerequisite for many fascinating applications for vision-based intelligent robots. Existing methods detect texts either using the local information only or casting it as a semantic segmentation problem. They tend to produce a large number of false alarms or cannot separate individual words accurately. In this work, we present an elegant segmentation-aided text detection solution that predicts the word-level bounding boxes using an end-to-end trainable deep convolutional neural network. It exploits the holistic view of a segmentation network in generating the text attention map (TAM) and uses the TAM to refine the convolutional features for the MultiBox detector through a multiplicative gating process. We conduct experiments on the large-scale and challenging COCO-Text dataset and demonstrate that the proposed method outperforms state-of-the-art methods significantly.

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