An English Handwriting Evaluation Algorithm Based on CNNs

Gao, Yingguo; Yu, Runze; Duan, Xiaohui

doi:10.1109/aiam48774.2019.00010

Cited by 3 publications

(5 citation statements)

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“…Learning-based DIQA approaches employ discriminative features to address different document degradation forms using techniques like deep learning [17,18]. In their work, Gao et al [3] introduced and evaluated a significant algorithm that relies on Convolutional Neural Networks (CNN) to evaluate English handwriting. They conducted a comparative study against well-established classification methods.…”

Section: -Deep-learning Approachesmentioning

confidence: 99%

“…This phase consists of two convolutional layers: a batch normalization layer, a Rectified Linear Unit (ReLU) activation function, and a fully connected dense layer. The first CNN layer employs 32 kernels of size (3,3) and includes a max pooling layer of size (2,2). This configuration yields 32 feature maps, each with dimensions of 32 pixels in width and 16 pixels in height.…”

Section: -Feature Extractionmentioning

confidence: 99%

“…After the CNN feature extraction phase, the processed feature sequence is passed to the Recurrent Neural Network (RNN) component. Specifically, we employ a bidirectional Long Short-Term Memory (Bi-LSTM) layer with 256 LSTM units in both forward and backward directions, as shown in Figure 2 (a), totaling 128 units in each direction using equations (1)(2)(3)(4)(5)(6). This bidirectional architecture allows the model to effectively capture dependencies between data entries in the sequence.…”

Section: -Learning Algorithmmentioning

confidence: 99%

“…Additionally, lighting conditions during document capture can affect the quality of the image due to environmental or physical influences. Handwriting is an important skill for conveying information and is associated with legibility and kinematics [2], which influence document quality [3].…”

Section: Introductionmentioning

confidence: 99%

“…Current methods for assessing document quality heavily rely on manual labor, having several drawbacks, including inconsistent evaluation standards among individuals, low expertise, and great labor costs. While recognition, document image classification, and character detection often involve image processing, handwritten document quality assessment is often overlooked [3]. Evaluation can occur at different granularities, including page, phrase, word, and character levels.…”

Section: Introductionmentioning

confidence: 99%

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Handwritten Arabic Bills Reader and Recognizer

Sweidan,

Hammam

2024

Journal of Computing and Communication

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In pursuit of Egypt's Vision 2030, which emphasizes the pivotal role of governance within state institutions and society, artificial intelligence (AI) stands as a transformative force. A central tenet of this vision involves harnessing AI technologies to accelerate the digitization of documents and their seamless integration into a unified system. This fosters more informed decision-making processes and revolutionizes processing and utilizing information. Our current research project aligns with this broader goal by deep learning capabilities to support organizations involved in enterprise applications. By incorporating AI-driven solutions, we aim to empower these organizations to manage their operations and optimize resource allocation efficiently. The proposed model eliminates manual input of handwritten invoices into ERP applications, resulting in substantive cost savings. Furthermore, the proposed model integrates an entity classification system enhanced by LSTM, significantly improving invoice data's clarity and accuracy. This streamlined approach saves valuable time and enhances the overall effectiveness of resource allocation and decision-making processes. In essence, by integrating AI into document management and enterprise operations, we are not only contributing to the realization of Egypt's vision but also spearheading a technological transformation that has far-reaching implications for governance, efficiency, and progress in the digital age.

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Section: -Deep-learning Approachesmentioning

confidence: 99%

Section: -Feature Extractionmentioning

confidence: 99%

Section: -Learning Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Handwritten Arabic Bills Reader and Recognizer

Sweidan,

Hammam

2024

Journal of Computing and Communication

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A Systematic Literature Review: Are Automated Essay Scoring Systems Competent in Real-Life Education Scenarios?

Xu,

Mahmud,

Lam Hoo

2024

IEEE Access

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Artificial intelligence technology is becoming increasingly essential to education. The outbreak of COVID-19 in recent years has led many schools to launch online education. Automated online assessments have become a hot topic of interest, and an increasing number of researchers are studying Automated Essay Scoring (AES). This work seeks to summarise the characteristics of current AES systems used in English writing assessment, identify their strengths and weaknesses, and finally, analyse the limits of recent studies and research trends. Search strings were used to retrieve papers on AES systems from 2018 to 2023 from four databases, 104 of which were chosen to be potential to address the posed research aims after study selection and quality evaluation. It is concluded that the existing AES systems, although achieving good results in terms of accuracy in specific contexts, are unable to meet the needs of teachers and students in real teaching scenarios. The improvements of these systems relate to the scalability of the system for assessing different topics or styles of the essays, the accuracy of the model's predicted scores, as well as the reliability of outcomes: improving the robustness of AES models with some adversarial inputs, the richness of AES system functionality, and the development of AES assist tools.INDEX TERMS Automated essay scoring, deep learning, machine learning, natural language processing, writing assessment.

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