Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (10)
  • Open Access

    ARTICLE

    CVTD: A Robust Car-Mounted Video Text Detector

    Di Zhou1, Jianxun Zhang1,*, Chao Li2, Yifan Guo1, Bowen Li1

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1821-1842, 2024, DOI:10.32604/cmc.2023.047236

    Abstract Text perception is crucial for understanding the semantics of outdoor scenes, making it a key requirement for building intelligent systems for driver assistance or autonomous driving. Text information in car-mounted videos can assist drivers in making decisions. However, Car-mounted video text images pose challenges such as complex backgrounds, small fonts, and the need for real-time detection. We proposed a robust Car-mounted Video Text Detector (CVTD). It is a lightweight text detection model based on ResNet18 for feature extraction, capable of detecting text in arbitrary shapes. Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation (CATA) and… More >

  • Open Access

    ARTICLE

    A Method for Detecting and Recognizing Yi Character Based on Deep Learning

    Haipeng Sun1,2, Xueyan Ding1,2,*, Jian Sun1,2, Hua Yu3, Jianxin Zhang1,2,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2721-2739, 2024, DOI:10.32604/cmc.2024.046449

    Abstract Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition, we present a deep learning-based approach for Yi character detection and recognition. In the detection stage, an improved Differentiable Binarization Network (DBNet) framework is introduced to detect Yi characters, in which the Omni-dimensional Dynamic Convolution (ODConv) is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features, thereby improving the accuracy of Yi character detection. Then, the feature pyramid network fusion module is used to further extract Yi character image features, improving target recognition… More >

  • Open Access

    ARTICLE

    Embedded System Based Raspberry Pi 4 for Text Detection and Recognition

    Turki M. Alanazi*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3343-3354, 2023, DOI:10.32604/iasc.2023.036411

    Abstract Detecting and recognizing text from natural scene images presents a challenge because the image quality depends on the conditions in which the image is captured, such as viewing angles, blurring, sensor noise, etc. However, in this paper, a prototype for text detection and recognition from natural scene images is proposed. This prototype is based on the Raspberry Pi 4 and the Universal Serial Bus (USB) camera and embedded our text detection and recognition model, which was developed using the Python language. Our model is based on the deep learning text detector model through the Efficient and Accurate Scene Text Detector… More >

  • Open Access

    ARTICLE

    CNN and Fuzzy Rules Based Text Detection and Recognition from Natural Scenes

    T. Mithila1,*, R. Arunprakash2, A. Ramachandran3

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1165-1179, 2022, DOI:10.32604/csse.2022.023308

    Abstract In today’s real world, an important research part in image processing is scene text detection and recognition. Scene text can be in different languages, fonts, sizes, colours, orientations and structures. Moreover, the aspect ratios and layouts of a scene text may differ significantly. All these variations appear assignificant challenges for the detection and recognition algorithms that are considered for the text in natural scenes. In this paper, a new intelligent text detection and recognition method for detectingthe text from natural scenes and forrecognizing the text by applying the newly proposed Conditional Random Field-based fuzzy rules incorporated Convolutional Neural Network (CR-CNN)… More >

  • Open Access

    ARTICLE

    ResNet CNN with LSTM Based Tamil Text Detection from Video Frames

    I. Muthumani1,*, N. Malmurugan2, L. Ganesan3

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 917-928, 2022, DOI:10.32604/iasc.2022.018030

    Abstract Text content in videos includes applications such as library video retrievals, live-streaming advertisements, opinion mining, and video synthesis. The key components of such systems include video text detection and acknowledgments. This paper provides a framework to detect and accept text video frames, aiming specifically at the cursive script of Tamil text. The model consists of a text detector, script identifier, and text recognizer. The identification in video frames of textual regions is performed using deep neural networks as object detectors. Textual script content is associated with convolutional neural networks (CNNs) and recognized by combining ResNet CNNs with long short-term memory… More >

  • Open Access

    ARTICLE

    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 sensitivity of target semantics in… More >

  • Open Access

    ARTICLE

    Text Detection and Classification from Low Quality Natural Images

    Ujala Yasmeen1, Jamal Hussain Shah1, Muhammad Attique Khan2, Ghulam Jillani Ansari1, Saeed ur Rehman1, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775

    Abstract Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image. (b) Subsequently, the standard maximal… More >

  • Open Access

    ARTICLE

    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 research and improvement of Connectionist… More >

  • Open Access

    ARTICLE

    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 can solve the problem that… More >

  • Open Access

    ARTICLE

    Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks

    Xianyu Wu1, Chao Luo1, Qian Zhang2, Jiliu Zhou1, Hao Yang1, 3, *, Yulian Li1

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 289-300, 2019, DOI:10.32604/cmc.2019.05990

    Abstract Words are the most indispensable information in human life. It is very important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level moral information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, great achievements have been made in text information detection and recognition. However, when dealing with text characters in natural scene images, there are still some limitations in the detection and recognition of natural scene images. Because natural scene image has more interference and complexity than… More >

Displaying 1-10 on page 1 of 10. Per Page