A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

AL-Shatnawi, Atallah; Al-Saqqar, Faisal; Alhusban, Safa’a

doi:10.3991/ijim.v14i16.16005

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…The principle of the KNN algorithm is to classify unknown points according to their distances from known issues. This means finding the k nearest neighbors of a query in the training dataset and predicting the results [31].…”

Section: Background Of the Approachmentioning

confidence: 99%

Edge-Fog-Cloud Data Analysis for eHealth-IoT

Zaoui,

Benabbou,

Ettaoufik

2023

Int. J. Onl. Eng.

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Thanks to advancements in artificial intelligence and the Internet of Things (IoT), eHealth is becoming an increasingly attractive area for researchers. However, different challenges arise when sensor-generated information is stored and analyzed using cloud computing. Latency, response time, and security are critical concerns that require attention. Fog and Edge Computing technologies have emerged in response to the requirement for resources near the network edge where data is collected, to minimize cloud challenges. This paper aims to assess the effectiveness of Machine Learning (ML) and Deep Learning (DL) techniques when executed in Edge or Fog nodes within the eHealth data. We compared the most efficient baseline techniques from the state-of-the-art on three eHealth datasets: Human Activity Recognition (HAR), University of Milano Bicocca Smartphone-based Human Activity Recognition (UniMiB SHAR), and MIT-BIH Arrhythmia. The experiment showed that for the HAR dataset, the Support Vector Machines (SVM) model was the best performer among the ML techniques, with low processing time and an accuracy of 96%. In comparison, the K-Nearest Neighbors (KNN) performed 94.43, and 96%, respectively, for SHAR and MIT-BIH datasets. Among the DL techniques, the Convolutional Neural Network with Fourier (CNNF) model performed the best, with accuracies of 94.49% and 98.72% for HAR and MIT-BIH. In comparison, CNN achieved 96.90% for the SHAR dataset.

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Section: Background Of the Approachmentioning

confidence: 99%

Edge-Fog-Cloud Data Analysis for eHealth-IoT

Zaoui,

Benabbou,

Ettaoufik

2023

Int. J. Onl. Eng.

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“…Al-Shatnawi and Al-Saqqar [3] suggested a holistic model for recognition of handwritten Arabic text based on the local binary pattern technique (LBP), and two machine-learning approaches: Support Vector Machines (SVM), and Artificial Neural Network (ANN).…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic-Based Evaluation Model of Handwritten Font Sizes Readability on Extra Small Devices

Bratić

Loknar

Ivančević

2022

Int. J. Interact. Mob. Technol.

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Handwritten fonts are appealing to designers, but their application in web design can cause a readability problems. Different handwritten letter cuts in different font sizes are not equally readable on all screen types. The problem often occurs on extra small devices such as mobile phones. Therefore, it is necessary to choose the combination of the font type and appropriate font size that will ensure responsiveness and be readable on different devices, especially the small ones. For this purpose, a study of readability of monoline handwritten font in seven letter cuts (thin, ultra-light, light, regular, semi-bold, bold, and ultra-bold) in several font sizes was conducted. Variable font was used because it contains all style versions of one typeface family, as opposed to standard font families that use different files for each style version. Also, variable font is suitable for use on web because one file with all the necessary typeface styles is significantly smaller in size than classic families with multiple files which shortens the font loading time. Furthermore, model of readability evaluation using the fuzzy logic based postprocessing method for segmentation values related to evaluation criteria is proposed. Prototype of a variable handwritten fonts are tested in responsive web environment, using CSS technology. The results show that handwritten font size readability evaluation has measurable output because the score combine various numeral factors affecting the readability of particular font size in several letter cuts. Using of proposed model in short time can show readability level of some font type in some font size on a new responsive web suitable for different screen sizes, including extra small devices.

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“…The Arabic language has a very complex writing characteristic. Therefore, Arabic language recognition is considered more complicated process comparing to the Chinese, Latin and many other languages [11]. One of the most important characteristics is cursive writing where cursive writing makes the letters are connected, which in turn makes letters recognition more difficult than the separated letters [15].…”

Section: Introductionmentioning

confidence: 99%

“…In general, the preprocessing stage is the most vital stage of the handwritten text recognition process because its results will strongly affect the following stage in the text recognition process [11]. The preprocessing stage includes several tasks.…”

Section: Introductionmentioning

confidence: 99%

Novel Algorithm for Baseline Detection of Offline Arabic Handwritten Text Recognition

Ahmad Mustafa Ali Al Masri,

Muhammad Suzuri Hitam,

Wan Nural Jawahir Hj Wan Yussof

et al. 2024

ARASET

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The baseline detection is one of the challenging tasks in the pre-processing stage of an online Arabic handwritten text recognition. The challenges include text skewness, touching letters or words, short words, sub-words, ligatures, isolated characters, small descenders, and diacritics. This paper presents a novel automated algorithm for baseline detection that is able to overcome the previously mentioned challenges. The proposed baseline algorithm mainly used a skeletonizing algorithm to estimate the baseline for each sub-word that appear in the image separately. The proposed algorithm had been tested using the benchmark handwritten Arabic database, i.e., IFN/ENIT database. The experimental results showed that it is able to reduce the average error between the actual baseline and the resulting baseline to 3.39 pixels for all sub-words and achieved an average of 3.6% in the ratio between the error pixels and the image height. In addition, the proposed algorithm shows that it is superior as compared to four others benchmarked baseline detection algorithms. It is anticipated that this algorithm will be able to be applied for many Arabic offline handwritten applications in the future.

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A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

Cited by 7 publications

References 25 publications

Edge-Fog-Cloud Data Analysis for eHealth-IoT

Edge-Fog-Cloud Data Analysis for eHealth-IoT

Fuzzy Logic-Based Evaluation Model of Handwritten Font Sizes Readability on Extra Small Devices

Novel Algorithm for Baseline Detection of Offline Arabic Handwritten Text Recognition

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