A Novel Dataset for English-Arabic Scene Text Recognition (EASTR)-42K and Its Evaluation Using Invariant Feature Extraction on Detected Extremal Regions

© 2019 IEEE. The recognition of text in natural scene images is a practical yet challenging task due to the large variations in backgrounds, textures, fonts, and illumination. English as a secondary language is extensively used in Gulf countries along with Arabic script. Therefore, this paper introduces English-Arabic scene text recognition 42K scene text image dataset. The dataset includes text images appeared in English and Arabic scripts while maintaining the prime focus on Arabic script. The dataset can be employed for the evaluation of text segmentation and recognition task. To provide an insight to other researchers, experiments have been carried out on the segmentation and classification of Arabic as well as English text and report error rates like 5.99% and 2.48%, respectively. This paper presents a novel technique by using adapted maximally stable extremal region (MSER) technique and extracts scale-invariant features from MSER detected region. To select discriminant and comprehensive features, the size of invariant features is restricted and considered those specific features which exist in the extremal region. The adapted MDLSTM network is presented to tackle the complexities of cursive scene text. The research on Arabic scene text is in its infancy, thus this paper presents benchmark work in the field of text analysis

Arabic cursive text recognition from natural scene images

© 2019 by the authors. This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years' publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers

Evaluation of handwritten Urdu text by integration of MNIST dataset learning experience

© 2019 IEEE. The similar nature of patterns may enhance the learning if the experience they attained during training is utilized to achieve maximum accuracy. This paper presents a novel way to exploit the transfer learning experience of similar patterns on handwritten Urdu text analysis. The MNIST pre-trained network is employed by transferring it's learning experience on Urdu Nastaliq Handwritten Dataset (UNHD) samples. The convolutional neural network is used for feature extraction. The experiments were performed using deep multidimensional long short term (MDLSTM) memory networks. The obtained result shows immaculate performance on number of experiments distinguished on the basis of handwritten complexity. The result of demonstrated experiments show that pre-trained network outperforms on subsequent target networks which enable them to focus on a particular feature learning. The conducted experiments presented astonishingly good accuracy on UNHD dataset

The Morphometric Study of Degenerative Lateral Canal Stenosis at L4-L5 and L5-S1 Using Magnetic Resonance Imaging (MRI): Feasibility Analysis for Posterior Surgical Decompression

This study was to evaluate the morphological features of degenerative spinal stenosis and adequacy of lateral canal stenosis decompression via unilateral and bilateral laminectomy. Measurements of facet joint angulation (FJA), mid facet point (MFP), mid facet point distance (MFPD), the narrowest point of the lateral spinal canal (NPLC) and the narrowest point of the lateral spinal canal distance (NPLCD) were performed. At L4L5 of the right and left side, the mean distance between the lateral border of the dura and MFP was 1.0 ± 0.2 cm and 1.0 ± 0.3cm respectively. The mean NPLC was seen at 0.7 ± 0.3 and 0.7 ± 0.3 cm cm from the dura. At L5S1 of the right and left side, the mean distance between the lateral border of the dura and MFP was 1.2± 0.2 and 1.3 ± 0.2 cm respectively. The mean NPLC was seen at 0.8 ± 0.4 and 0.9 ± 0.5 cm from the dura. Unilateral laminectomy may result in incomplete decompression

Lumbar Spinal Stenosis: The Reliability, Sensitivity and Specificity of the Nerve Root Sedimentation Sign

Introduction: This study is to evaluate the reliability, sensitivity and specificity of nerve root sedimentation sign (NRS) in our populations. The NRS is a radiological sign to diagnose lumbar spinal stenosis (LSS). It is claimed to be reliable with high sensitivity and specificity. Materials and Methods: A total of 82 MRI images from 43 patients in Group A (LSS) and 39 patients in Group B (non LSS) were analysed and compared for the presence of the NRS sign. Two assessors were used to evaluate intra and inter-assessor reliability of this sign based on 56 (33 patients, Group A and 23 patients, Group B). The findings were statistically analysed using SPSS software. Results: There was a significant association between spinal claudication and leg numbness with LSS (p<0.001 and Kappa=0.857, p<0.001). The inter-assessor reliability was also good (Kappa of 0.786, p<0.001). Conclusion: The NRS sign has high sensitivity and specificity for diagnosing LSS. The sign also has good intra and inter-assessor reliability

Pengaturcaraan Web Hypertext Preprocessor (PHP)

Buku ini memperkenalkan kepada pembaca tentang asas pengaturcaraan web dalam Hypertext Preprocessor (PHP). Perbincangan awal dimula dengan memperkenalkan teknologi web dan perisian sumber terbuka. Kemudiannya, diikuti pengenalan kepada PHP yang merangkumi konsep, sejarah dan keperluan asas sebelum menulis atur cara PHP. Selain itu, terdapat beberapa bab yang menghuraikan pelbagai elemen bahasa pengaturcaraan. Antaranya struktur kawalan, fungsi, rentetan, dan tatasusunan. Bab akhir buku ini pula menerangkan tentang cara membangunkan aplikasi web menggunakan PHP, iaitu sistem penjanaan nombor dan variasi rawak. Buku ini juga turut menyediakan contoh atur cara dan latihan pengaturcaraan bagi memahir dan mengukuhkan lagi penguasaan pembaca dalam pengaturcaraan web. Buku ini sesuai sebagai bahan pengajaran dan pembelajaran oleh pensyarah dan pelajar, kepada pengaturcara yang ingin mempelajari bahasa penulisan skrip PHP dengan lebih mendalam, serta pembaca umum yang berminat untuk mengenali PHP dan ingin mempelajari asas pengaturcaraan web dengan mudah dan berkesan

Using social media as a tool for improving academic performance through collaborative learning in Malaysian higher education

Nowadays, many higher education institutions are still depending on the traditionally-based learning management systems that actually do not use full capabilities of social media in engaging researchers in collaborative learning. Due to recent rise in social media usage, especially among researchers and lecturers of educational institutions, a great deal of research was conducted to explore how to take advantage of social media and use it to improve the researchers ‘academic performance through collaborative learning. To achieve this objective, it is important to explore the actual relationship between two variables: social media and academic performance. This study is aimed at examining the relationship between using social media and improving academic performance. A survey was conducted among the research students of Universiti Teknologi Malaysia, and 323 valid responses were received. Structural equation modeling was employed to test the relationship between three constructs: social media, collaborative learning, and academic performance. The results showed a significant effect of social media on the students’ academic performance with collaborative learning as the mediating variable. Without good collaborative learning, an education institution cannot take advantage of social media for improving academic performance

Using social media as a tool for improving academic performance through collaborative learning in Malaysian higher education

Nowadays, many higher education institutions are still depending on the traditionally-based learning management systems that actually do not use full capabilities of social media in engaging researchers in collaborative learning. Due to recent rise in social media usage, especially among researchers and lecturers of educational institutions, a great deal of research was conducted to explore how to take advantage of social media and use it to improve the researchers ‘academic performance through collaborative learning. To achieve this objective, it is important to explore the actual relationship between two variables: social media and academic performance. This study is aimed at examining the relationship between using social media and improving academic performance. A survey was conducted among the research students of Universiti Teknologi Malaysia, and 323 valid responses were received. Structural equation modeling was employed to test the relationship between three constructs: social media, collaborative learning, and academic performance. The results showed a significant effect of social media on the students’ academic performance with collaborative learning as the mediating variable. Without good collaborative learning, an education institution cannot take advantage of social media for improving academic performance

Solving the complexity of heterogeneity data on learning environment using ontology

Distributed and various systems on learning environment are the current issues to produce big data and heterogeneity data problem. Heterogeneity on learning environment is about numerous learning applications and various learning information to support a learning process in educational institutions. There are a lot of relationships are formed between elements on learning environment. The elements on learning environment consist of learning data, learning applications, data sources, learning concept, and data heterogeneity aspect on learning environment. These elements are interrelated and produce complex relationship between each other. A complex relationship problem between elements on learning environment makes a process of analysis and identification difficult to be done. Existing method to drawing this heterogeneity problem make confuse and misunderstanding readers. To solved this problem, researcher using ontology knowledge to describe and draw a semantic relationship that represent the complexity of data relationship on learning environment. The result of this analysis is to develop ontology knowledge to solve heterogeneity data problem specific in complexity relationship on learning environment. This result can give better understanding to the readers about complex relationship between elements on learning environment