Text Detection Using Multi-Stage Region Proposal Network Sensitive to Text Scale

Recently, attention has surged concerning intelligent sensors using text detection. However, there are challenges in detecting small texts. To solve this problem, we propose a novel text detection CNN (convolutional neural network) architecture sensitive to text scale. We extract multi-resolution fe...

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Published inSensors (Basel, Switzerland) Vol. 21; no. 4; p. 1232
Main Authors Nagaoka, Yoshito, Miyazaki, Tomo, Sugaya, Yoshihiro, Omachi, Shinichiro
Format Journal Article
LanguageEnglish
Published Switzerland MDPI 09.02.2021
MDPI AG
Subjects
convolutional neural networks
multiple scales
scene text detection
multiple scales
convolutional neural networks
scene text detection
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Summary:Recently, attention has surged concerning intelligent sensors using text detection. However, there are challenges in detecting small texts. To solve this problem, we propose a novel text detection CNN (convolutional neural network) architecture sensitive to text scale. We extract multi-resolution feature maps in multi-stage convolution layers that have been employed to prevent losing information and maintain the feature size. In addition, we developed the CNN considering the receptive field size to generate proposal stages. The experimental results show the importance of the receptive field size.
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This paper is an extended version of our paper published in Nagaoka, Y.; Miyazaki, T.; Sugaya, Y.; Omachi, S. Text Detection by Faster R-CNN with Multiple Region Proposal Networks. In Proceedings of the 7th International Workshop on Camera-Based Document Analysis and Recognition (CBDAR), Kyoto, Japan, 9–15 November 2017; pp. 15–20.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21041232