CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Dataset

Kizilirmak, Firat; Yanıkoğlu, Berrin

doi:10.21203/rs.3.rs-2274499/v1

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…We assessed the performance of the text animation generation model using character error rate (CER) and word error rate (WER), which corresponds to normalized Levenshtein distances between the predicted and ground truth character sequences. Models [28][29][30] were trained and evaluated on the corresponding Aachen splits of the IAM dataset. Our approach is compared to state-of-art methods with varying characteristics, as displayed in Table 2.…”

Section: Animation Generation Experimentsmentioning

confidence: 99%

GestureTeach: A gesture guided online teaching interactive model

Liu,

Yao,

Zhang

et al. 2023

Computer Animation & Virtual

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Online education has become more popular and effective due to the availability of high‐speed internet and technological innovations, which allow people from different locations to access educational resources and opportunities. However, online classes often face challenges such as limited interactivity and display options, which can affect the quality and effectiveness of the online learning experience. In this article, we propose GestureTeach, a new pedagogical paradigm that enables free handwriting interaction and animation generation for online teaching. GestureTeach uses gestures as a natural and intuitive way of interaction, which enhances the teacher's intention expression and the student's engagement. GestureTeach also generates animations from handwritten sketches, which improves the display effects of the interaction and the student's knowledge comprehension. We conducted a two‐stage study with 15 teachers and 90 students to evaluate the effectiveness of GestureTeach in facilitating classroom interaction. The results show that GestureTeach is preferred by both teachers and students over traditional online teaching methods and has the potential to transform the online teaching landscape by providing a seamless and interactive experience.

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Section: Animation Generation Experimentsmentioning

confidence: 99%

GestureTeach: A gesture guided online teaching interactive model

Liu,

Yao,

Zhang

et al. 2023

Computer Animation & Virtual

View full text Add to dashboard Cite

show abstract

“…A primary obstacle in AHR research is the limited availability of large-scale, diverse datasets. Training deep learning models, especially those involving Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) [10]- [13], requires ample data to learn robust representations and generalize effectively. The scarcity of labeled Arabic handwritten datasets hinders model performance and limits practical applicability.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks

Rabi,

Amrouche

2024

EJAI

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Arabic Handwritten Recognition (AHR) presents unique challenges due to the complexity of Arabic script and the limited availability of training data. This paper proposes an approach that integrates generative adversarial networks (GANs) for data augmentation within a robust CNN-BLSTM architecture, aiming to significantly improve AHR performance. We employ a CNN-BLSTM network coupled with connectionist temporal classification (CTC) for accurate sequence modeling and recognition. To address data limitations, we incorporate a GANs based data augmentation module trained on the IFN-ENIT Arabic handwriting dataset to generate realistic and diverse synthetic samples, effectively augmenting the original training corpus. Extensive evaluations on the IFN-ENIT benchmark demonstrate the efficacy of adopted approach. We achieve a recognition rate of 95.23%, surpassing the baseline model by 3.54%. This research presents a promising approach to data augmentation in AHR and demonstrates a significant improvement in word recognition accuracy, paving the way for more robust and accurate AHR systems.

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CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Dataset

Cited by 2 publications

References 31 publications

GestureTeach: A gesture guided online teaching interactive model

GestureTeach: A gesture guided online teaching interactive model

Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks

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