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  • Open Access


    YOLOv5ST: A Lightweight and Fast Scene Text Detector

    Yiwei Liu1, Yingnan Zhao1,*, Yi Chen1, Zheng Hu1, Min Xia2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 909-926, 2024, DOI:10.32604/cmc.2024.047901

    Abstract Scene text detection is an important task in computer vision. In this paper, we present YOLOv5 Scene Text (YOLOv5ST), an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection. Our primary goal is to enhance inference speed without sacrificing significant detection accuracy, thereby enabling robust performance on resource-constrained devices like drones, closed-circuit television cameras, and other embedded systems. To achieve this, we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation, including replacing standard convolution with depth-wise convolution, adopting the C2 sequence module in place More >

  • Open Access


    Adaptive Multi-Scale HyperNet with Bi-Direction Residual Attention Module for Scene Text Detection

    Junjie Qu, Jin Liu*, Chao Yu

    Journal of Information Hiding and Privacy Protection, Vol.3, No.2, pp. 83-89, 2021, DOI:10.32604/jihpp.2021.017181

    Abstract Scene text detection is an important step in the scene text reading system. There are still two problems during the existing text detection methods: (1) The small receptive of the convolutional layer in text detection is not sufficiently sensitive to the target area in the image; (2) The deep receptive of the convolutional layer in text detection lose a lot of spatial feature information. Therefore, detecting scene text remains a challenging issue. In this work, we design an effective text detector named Adaptive Multi-Scale HyperNet (AMSHN) to improve texts detection performance. Specifically, AMSHN enhances the More >

  • Open Access


    A Modified Method for Scene Text Detection by ResNet

    Shaozhang Niu1, *, Xiangxiang Li1, Maosen Wang1, Yueying Li2

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2233-2245, 2020, DOI:10.32604/cmc.2020.09471

    Abstract In recent years, images have played a more and more important role in our daily life and social communication. To some extent, the textual information contained in the pictures is an important factor in understanding the content of the scenes themselves. The more accurate the text detection of the natural scenes is, the more accurate our semantic understanding of the images will be. Thus, scene text detection has also become the hot spot in the domain of computer vision. In this paper, we have presented a modified text detection network which is based on further… More >

  • Open Access


    A Method of Text Extremum Region Extraction Based on JointChannels

    Xueming Qiao1, Yingxue Xia1, Weiyi Zhu2, Dongjie Zhu3, *, Liang Kong1, Chunxu Lin3, Zhenhao Guo3, Yiheng Sun3

    Journal on Artificial Intelligence, Vol.2, No.1, pp. 29-37, 2020, DOI:10.32604/jai.2020.09955

    Abstract Natural scene recognition has important significance and value in the fields of image retrieval, autonomous navigation, human-computer interaction and industrial automation. Firstly, the natural scene image non-text content takes up relatively high proportion; secondly, the natural scene images have a cluttered background and complex lighting conditions, angle, font and color. Therefore, how to extract text extreme regions efficiently from complex and varied natural scene images plays an important role in natural scene image text recognition. In this paper, a Text extremum region Extraction algorithm based on Joint-Channels (TEJC) is proposed. On the one hand, it More >

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