Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances

Ahmed, Rami; Dashtipour, Kia; Gogate, Mandar; Raza, Ali; Zhang, Rui; Huang, Kaizhu; Hawalah, Ahmad; Adeel, Ahsan; Hussain, Amir

doi:10.1007/978-3-030-39431-8_44

Cited by 24 publications

(16 citation statements)

References 30 publications

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“…In order to do the stemming process, a Persian Python tool called HAZM is used. HAZM contains text cleaning, sentence word tokenizer, word lemmatizer, part-of-speech tagger, shallow parser and dependency parser [ 72 , 73 , 74 , 75 , 76 ]. Figure 2 shows examples of Persian sentences.…”

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Dashtipour

Gogate

Adeel

et al. 2021

Entropy

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Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.

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Section: Methodsmentioning

confidence: 99%

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Dashtipour

Gogate

Adeel

et al. 2021

Entropy

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“…They have been adapted for feature extraction in many text recognition systems. We can cite for example: scene text recognition [12], [13], video text recognition [14], and offline handwriting text recognition [15]- [17]. However, CNN-based or DL-based approaches are still deficient.…”

Section: ) Feature Extractionmentioning

confidence: 99%

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

et al. 2021

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One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to the cursive nature of the Arabic writing, character similarities, unlimited vocabulary, use of multi-size and mixed-fonts, etc. To handle these challenges, an automatic Arabic text recognition requires building a robust system by computing discriminative features and applying a rigorous classifier together to achieve an improved performance. In this work, we introduce a new deep learning based system that recognizes Arabic text contained in images. We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition. Our proposed system, termed BoF-deep SAE-HMM, is tested on four datasets, namely the printed Arabic line images Printed KHATT (P-KHATT), the benchmark printed word images Arabic Printed Text Image (APTI), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST).

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“…There are different features that can be selected to identify skin hydration. For example, minimum, mean, variance, entropy, standard deviation, percentile, median, mode, and kurtosis (Ahmed et al, 2019). The importance of every feature is analyzed from different sizes of the window.…”

Section: Feature Selection For Skin Hydrationmentioning

confidence: 99%

A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration

et al. 2021

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Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, with a prevalence of 1–2% in the community, increasing the risk of stroke and myocardial infarction. Early detection of AF, typically causing an irregular and abnormally fast heart rate, can help reduce the risk of strokes that are more common among older people. Intelligent models capable of automatic detection of AF in its earliest possible stages can improve the early diagnosis and treatment. Luckily, this can be made possible with the information about the heart's rhythm and electrical activity provided through electrocardiogram (ECG) and the decision-making machine learning-based autonomous models. In addition, AF has a direct impact on the skin hydration level and, hence, can be used as a measure for detection. In this paper, we present an independent review along with a comparative analysis of the state-of-the-art techniques proposed for AF detection using ECG and skin hydration levels. This paper also highlights the effects of AF on skin hydration level that is missing in most of the previous studies.

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Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances

Cited by 24 publications

References 30 publications

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration

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