Tuğba Çelikten¹, Aytuğ Onan^2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3351-3377, 2024, DOI:10.32604/cmc.2024.051574 - 15 August 2024

Abstract The purpose of this study is to develop a reliable method for distinguishing between AI-generated, paraphrased, and human-written texts, which is crucial for maintaining the integrity of research and ensuring accurate information flow in critical fields such as healthcare. To achieve this, we propose HybridGAD, a novel hybrid model that combines Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU) architectures with an attention mechanism. Our methodology involves training this hybrid model on a dataset of radiology abstracts, encompassing texts generated by AI, paraphrased by AI, and written by humans. The… More >

Sana Sahar Guia¹, Abdelkader Laouid¹, Mohammad Hammoudeh^2,*, Mostafa Kara^1,3

Computer Systems Science and Engineering, Vol.48, No.4, pp. 915-935, 2024, DOI:10.32604/csse.2024.046414 - 17 July 2024

Abstract Artificial intelligence has been involved in several domains. Despite the advantages of using artificial intelligence techniques, some crucial limitations prevent them from being implemented in specific domains and locations. The accuracy, poor quality of gathered data, and processing time are considered major concerns in implementing machine learning techniques, certainly in low-end smart devices. This paper aims to introduce a novel pre-treatment technique dedicated to image text detection that uses the images’ pixel divergence and similarity to reduce the image size. Mitigating the image size while keeping its features improves the model training time with an… More >

Yiwei Liu¹, Yingnan Zhao^1,*, Yi Chen¹, Zheng Hu¹, Min Xia²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 909-926, 2024, DOI:10.32604/cmc.2024.047901 - 25 April 2024

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 >

Di Zhou¹, Jianxun Zhang^1,*, Chao Li², Yifan Guo¹, Bowen Li¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1821-1842, 2024, DOI:10.32604/cmc.2023.047236 - 27 February 2024

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… More >

Haipeng Sun^1,2, Xueyan Ding^1,2,*, Jian Sun^1,2, Hua Yu³, Jianxin Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2721-2739, 2024, DOI:10.32604/cmc.2024.046449 - 27 February 2024

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… More >

Turki M. Alanazi^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3343-3354, 2023, DOI:10.32604/iasc.2023.036411 - 15 March 2023

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… More >

T. Mithila^1,*, R. Arunprakash², A. Ramachandran³

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1165-1179, 2022, DOI:10.32604/csse.2022.023308 - 08 February 2022

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… More >

I. Muthumani^1,*, N. Malmurugan², L. Ganesan³

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 917-928, 2022, DOI:10.32604/iasc.2022.018030 - 22 September 2021

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 More >

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 - 30 July 2021

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 >

Ujala Yasmeen¹, Jamal Hussain Shah¹, Muhammad Attique Khan², Ghulam Jillani Ansari¹, Saeed ur Rehman¹, Muhammad Sharif¹, Seifedine Kadry³, Yunyoung Nam^4,*

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775 - 24 December 2020

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.… More >