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  • Open Access

    ARTICLE

    MNIST Handwritten Digit Classification Based on Convolutional Neural Network with Hyperparameter Optimization

    Haijian Shao1, Edwin Ma2, Ming Zhu1, Xing Deng3, Shengjie Zhai1,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3595-3606, 2023, DOI:10.32604/iasc.2023.036323 - 15 March 2023

    Abstract Accurate handwriting recognition has been a challenging computer vision problem, because static feature analysis of the text pictures is often inadequate to account for high variance in handwriting styles across people and poor image quality of the handwritten text. Recently, by introducing machine learning, especially convolutional neural networks (CNNs), the recognition accuracy of various handwriting patterns is steadily improved. In this paper, a deep CNN model is developed to further improve the recognition rate of the MNIST handwritten digit dataset with a fast-converging rate in training. The proposed model comes with a multi-layer deep arrange More >

  • Open Access

    ARTICLE

    Person-Dependent Handwriting Verification for Special Education Using Deep Learning

    Umut Zeki1,*, Tolgay Karanfiller2, Kamil Yurtkan1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1121-1135, 2023, DOI:10.32604/iasc.2023.032554 - 29 September 2022

    Abstract Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent. However, in crowded classrooms, it is difficult for a teacher to deal with each student individually. This problem can be overcome by using supportive education applications. However, the majority of such applications are not designed for special education and therefore they are not efficient as expected. Special education students differ from their peers in terms of their development, characteristics, and educational qualifications. The handwriting skills of individuals with special needs are lower than their peers. This makes the… More >

  • Open Access

    ARTICLE

    Automated Handwriting Recognition and Speech Synthesizer for Indigenous Language Processing

    Bassam A. Y. Alqaralleh1,*, Fahad Aldhaban1, Feras Mohammed A-Matarneh2, Esam A. AlQaralleh3

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3913-3927, 2022, DOI:10.32604/cmc.2022.026531 - 29 March 2022

    Abstract In recent years, researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities. The recent developments of artificial intelligence (AI), natural language processing (NLP), and computational linguistics (CL) find useful in the analysis of regional low resource languages. Automatic lexical task participation might be elaborated to various applications in the NLP. It is apparent from the availability of effective machine recognition models and open access handwritten databases. Arabic language is a commonly spoken Semitic language, and it is written with the cursive Arabic alphabet from right to left. Arabic… More >

  • Open Access

    ARTICLE

    Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

    Reya Sharma1, Baijnath Kaushik1, Naveen Kumar Gondhi1, Muhammad Tahir2,*, Mohammad Khalid Imam Rahmani2

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5855-5873, 2022, DOI:10.32604/cmc.2022.024232 - 14 January 2022

    Abstract Even though several advances have been made in recent years, handwritten script recognition is still a challenging task in the pattern recognition domain. This field has gained much interest lately due to its diverse application potentials. Nowadays, different methods are available for automatic script recognition. Among most of the reported script recognition techniques, deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms. However, the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error, which renders them unfeasible. This approach often More >

  • Open Access

    ARTICLE

    Single-Choice Aided Marking System Research Based on Back Propagation Neural Network

    Yunzuo Zhang*, Yi Li, Wei Guo, Lei Huo, Jiayu Zhang, Kaina Guo

    Journal of Cyber Security, Vol.3, No.1, pp. 45-54, 2021, DOI:10.32604/jcs.2021.017071 - 30 April 2021

    Abstract In the field of educational examination, automatic marking technology plays an essential role in improving the efficiency of marking and liberating the labor force. At present, the implementation of the policy of expanding erolments has caused a serious decline in the teacher-student ratio in colleges and universities. The traditional marking system based on Optical Mark Reader technology can no longer meet the requirements of liberating the labor force of teachers in small and medium-sized examinations. With the development of image processing and artificial neural network technology, the recognition of handwritten character in the field of… More >

  • Open Access

    ARTICLE

    Recognition of Offline Handwritten Arabic Words Using a Few Structural Features

    Abderrahmane Saidi*, Abdelmouneim Moulay Lakhdar, Mohammed Beladgham

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2875-2889, 2021, DOI:10.32604/cmc.2021.013744 - 28 December 2020

    Abstract Handwriting recognition is one of the most significant problems in pattern recognition, many studies have been proposed to improve this recognition of handwritten text for different languages. Yet, Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts. The present paper suggests a feature extraction technique for offline Arabic handwriting recognition. A handwriting recognition system for Arabic words using a few important structural features and based on a… More >

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