A Novel Deep-learning based Approach for Automatic Diacritization of Arabic Poems using Sequence-to-Sequence Model

Mahmoud, Mohamed S.; Negied, Nermin K.

doi:10.14569/ijacsa.2023.0140105

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…One of the groundbreaking approaches in deep learning [32] for handwriting recognition is the use of sequence-to-sequence models with attention mechanisms. These models, often based on architectures like the Encoder-Decoder framework, are capable of taking in a sequence of input data (handwritten text) and generating an output sequence (recognized text) [33]. Attention mechanisms enable the model to focus on relevant parts of the input sequence during the decoding process, allowing for more precise and context-aware recognition [34].…”

Section: Deep Learning Approaches To Handwriting Recognitionmentioning

confidence: 99%

Machine Learning Methods for Handwriting Recognition

Peng

2023

Preprint

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Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.

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Section: Deep Learning Approaches To Handwriting Recognitionmentioning

confidence: 99%

Machine Learning Methods for Handwriting Recognition

Peng

2023

Preprint

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Leveraging Hybrid Adaptive Sine Cosine Algorithm with Deep Learning for Arabic Poem Meter Detection

Al-shathry,

Al-onazi,

Hassan

et al. 2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Poetry is a significant aspect of any language. Many cultures and the history of nations are recognized in poems. Compared to prose, each poem has a rhythmic structure that is quite different. The language has its set of lyrical structures for poems, known as meters. Detecting the meters of Arabic poems is a complicated and lengthy procedure. The text must be encrypted using the Arudi method to classify the poem's meter, which requires complex rule-based transformation before another set of rules classifies the meters. Applying deep learning (DL) to meter classification in Arabic poems includes constructing a neural network to discern rhythmic patterns inherent in various meters. The model can extract essential features, like word lengths or syllable patterns, by tokenizing and preprocessing text datasets. Architectures such as Long Short-Term Memory Networks (LSTM) or Recurrent Neural Networks (RNNs) are fitting solutions to capture temporal relations in poetic verses. This research introduces a Hybrid Meta-heuristics with Deep Learning for the Arabic Poem Meter Detection and Classification (HMDL-APMDC) model. The main intention of the HMDL-APMDC system is to recognize various kinds of meters in Arabic poems. The HMDL-APMDC technique primarily preprocesses the input dataset to make it compatible with the classification process. Besides, the HMDL-APMDC technique applies Convolution and Attention with a Bi-directional Gated Recurrent Unit (CAT-BiGRU) for the automated recognition of meter classes. Furthermore, the adaptive sine-s-cosine particle swarm optimization (ASCA-PSO) algorithm is applied to optimize the hyperparameter tuning of the CAT-BiGRU model, enhancing the meter detection results. A detailed simulation analysis is made to highlight the improved performance of the HMDL-APMDC technique. The empirical outcomes stated that the HMDL-APMDC technique had a superior outcome of 98.53% over recent models under the MetRec dataset.

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A Novel Deep-learning based Approach for Automatic Diacritization of Arabic Poems using Sequence-to-Sequence Model

Cited by 2 publications

References 15 publications

Machine Learning Methods for Handwriting Recognition

Machine Learning Methods for Handwriting Recognition

Leveraging Hybrid Adaptive Sine Cosine Algorithm with Deep Learning for Arabic Poem Meter Detection

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