Fast learning neural network based on texture for Arabic calligraphy identification

Hussein, Ahmed Kawther

doi:10.11591/ijeecs.v21.i3.pp1794-1799

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…A few unique sorts of layers are frequently utilized in CNNs [20,21], such as loss, convolutional, ReLu (Rectified Linear Units), pooling, and fully connected layer [22]. Deep neural network used in many fields such as the study [23][24][25][26][27][28].…”

Section: Figure 3 Overview Of a Convolution Neural Network [16]mentioning

confidence: 99%

Facial Emotion Recognition Using HOG and Convolution Neural Network

Ghalib¹,

Ibrahim²,

Noor³

2023

ISI

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Due to numerous difficulties, including the variation in face shapes between individuals, the challenge of recognizing dynamic facial attributes, the poor quality of digital images, etc., detecting human emotion depending on facial expression is difficult for the computer vision community. Thus, in this study, we propose an approach for emotion recognition depending on facial expression using histogram of oriented gradients and convolution neural network (HOG-CNN). The HOG-CNN composed of three stages, median filter, HOG, and CNN. The first stage is preprocessing using median filter. The second stage is feature extraction using HOG. The third stage is classification using CNN. The proposed method was tested and evaluated on the UMD face database. The system attained a high performance with a mean average accuracy of 98.07%, average precision of 94.78%, and average recall of 97.15%.

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Section: Figure 3 Overview Of a Convolution Neural Network [16]mentioning

confidence: 99%

Facial Emotion Recognition Using HOG and Convolution Neural Network

Ghalib¹,

Ibrahim²,

Noor³

2023

ISI

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“…Ahmed Kawther Hussein arap hattı stil sınıflandırma amaçlı çalışmada LBP,LPQ,BSIF gibi özellik çıkarımı yöntemlerini kullanmıştır. Sınıflandırma adımında ise Hızlı öğrenme sinir ağı (Fast Learning Neural Network) ve uç öğrenme makine yöntemlerini karşılaştırmıştır (Hussein 2021).…”

Section: Literatürdeki çAlışmalarunclassified

Hat Sanati Eserleri̇ni̇n Yapay Zeka Algori̇tmalari İle Anali̇zi̇

Zeynep GÜRER,

GÖKBAY

2023

SOBIDER

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Arap yazısına bağlı olarak doğan güzel yazma sanatı Hat Sanatı Türk-İslam sanatı ve medeniyeti hakkında birçok bilgi içermektedir. Bu eserlerdeki mesajların anlaşılması yerli halkın kendi tarihini anlaması açısından önemlidir. Bunun yanında turistlerin yoğun ilgi gösterdiği tarihi mekanlar da bu eserler ile donatılmıştır. Bu eserlerdeki yazılar ile gezilen mekanların önemi bilgileri daha iyi anlaşılacaktır. Son zamanlarda yapıların özellikle camilerin girişlerinde yapı içerisindeki hat eserlerinin anlamlarının yazılı olduğu levhaların yerleştirilmeye başlanması bu eserlerin anlaşılmaya olan ihtiyacını göstermektedir. Türkiye'de ve Dünya'daki milyonlarca eserin anlamlandırılması manuel bir işlemle çok zordur. Bu sebeple bu işlemin otomatikleştirilmesi adına bu çalışmada başlangıç adımı atılmıştır. Hat metinlerinin karmaşık görüntüden yazıya çevrilmesindeki aşamalardan olan yazıların stillerinin belirlenmesi ve yazıdaki harflerin tanınması problemleri Yapay Zeka algoritmaları ile incelenmiştir.

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“…The quick progress of machine learning-based systems has made HCI applicable to many new domains, including unmanned aerial vehicles (UAV) detection [15], Lung segmentation [16], speaker identification [17], video summarization [18], handwriting recognition [19], contextual anomaly detection [20], temperature prediction [21], food recognition [22], face retrieval system [23], wildfire detection [24], and many more. Therefore, an unsupervised neural network that contrasts favorably with other techniques has been used.…”

Section: Introductionmentioning

confidence: 99%

Facial recognition based on enhanced neural network

AL-Qinani,

Saleh,

Saleh

2024

IJ-AI

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Accurate automatic face recognition (FR) has only become a practical goal of biometrics research in recent years. Detection and recognition are the primary steps for identifying faces in this research, and The Viola-Jones algorithm implements to discover faces in images. This paper presents a neural network solution called modify bidirectional associative memory (MBAM). The basic idea is to recognize the image of a human's face, extract the face image, enter it into the MBAM, and identify it. The output ID for the face image from the network should be similar to the ID for the image entered previously in the training phase. The tests have conducted using the suggested model using 100 images. Results show that FR accuracy is 100% for all images used, and the accuracy after adding noise is the proportions that differ between the images used according to the noise ratio. Recognition results for the mobile camera images were more satisfactory than those for the Face94 dataset.

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Fast learning neural network based on texture for Arabic calligraphy identification

Cited by 4 publications

References 20 publications

Facial Emotion Recognition Using HOG and Convolution Neural Network

Facial Emotion Recognition Using HOG and Convolution Neural Network

Hat Sanati Eserleri̇ni̇n Yapay Zeka Algori̇tmalari İle Anali̇zi̇

Facial recognition based on enhanced neural network

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