A Survey on Arabic Handwritten Character Recognition

Ali, Amani Ali Ahmed; Suresha, M; Ahmed, Heba Ali Mohsin

doi:10.1007/s42979-020-00168-1

Cited by 14 publications

(15 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ali et al [13] discussed the character recognition process for Arabic script in detail. In this study, some established techniques were summarized, whereas a few techniques were also examined to build a powerful HCR mechanism.…”

Section: Related Workmentioning

confidence: 99%

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

Maray¹,

Al-onazi²,

Alshahrani³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors. The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters. At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a 5468 CMC, 2023, vol.74, no.3 maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

Maray¹,

Al-onazi²,

Alshahrani³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…For most computer vision applications, deep learning architectures have been the de facto choice (Ali et al, 2020;Balaha et al, 2020). Their effectiveness, however, is strongly reliant on access to labelled large-scale datasets.…”

Section: Participants' Character Recognitionmentioning

confidence: 99%

A study of children emotion and their performance while handwriting Arabic characters using a haptic device

et al. 2022

View full text Add to dashboard Cite

Emotion and focus of children during handwriting are essential for language learning. Handwriting for young children is challenging because it needs deep motivation and willingness to complete the task regardless of its difficulty. Recently, emerged haptic guidance systems have a good potential to offer children a better sense of engagement to keep their interests awake. Yet, handwriting in 3D is more challenging for children due to many reasons including demotivation, out of focus, and visuomotor coordination difficulties. In this paper, we study the effectiveness of a haptic device in analyzing schoolchildren emotion, attentiveness and handwriting performance of Arabic letters. We conducted the experiments for a period of four weeks with an immersive environment where the subjects practiced writing in VR environment using a haptic device-controlled stylus. We assessed the childrens’ emotions to get insights into their engagement during such hard learning environment. We found that our approach improved the participants’ fine-motor skills and handwriting quality. However, our analysis revealed that such task was effective on detecting emotions (angry/neutral) only as a negative/positive contributor of performance. Overall, the obtained findings can well contribute to the understanding of the relationship between students’ emotions and other variables in an attempt to support the development of adaptive learning technologies.

show abstract

“…Thus, using automated recognition techniques for handwritten data in many applications is crucial. These techniques convert handwritten data (e.g., texts, words, characters, or digits) into corresponding digital representations, which can be accurately processed offline, such as scanned handwritten documents, or online, such as handwriting data input via electronic pen tip [ 2 , 3 , 4 ]. Developing automatic handwriting recognition systems is a difficult task in computer vision due to the wide variety of handwriting sizes and styles, besides the characteristics of the language to be recognized [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Handwritten character recognition is one of the most challenging research fields in document image processing. Most investigations in this field have been conducted on different languages (e.g., English, French, and Chinese), but only a little work has been conducted on other languages like Arabic [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Alwagdani,

Jaha

2023

Sensors

View full text Add to dashboard Cite

Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults’ handwriting, children’s handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children’s handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children’s handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.

show abstract

A Survey on Arabic Handwritten Character Recognition

Cited by 14 publications

References 44 publications

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

A study of children emotion and their performance while handwriting Arabic characters using a haptic device

Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Contact Info

Product

Resources

About