Mohammad Fasha - Academia.edu (original) (raw)
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Address: Amman, Al Balqa', Jordan
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Papers by Mohammad Fasha
ArXiv, 2020
Arabic text recognition is a challenging task because of the cursive nature of Arabic writing sys... more Arabic text recognition is a challenging task because of the cursive nature of Arabic writing system, its joint writing scheme, the large number of ligatures and many other challenges. Deep Learning DL models achieved significant progress in numerous domains including computer vision and sequence modelling. This paper presents a model that can recognize Arabic text that was printed using multiple font types including fonts that mimic Arabic handwritten scripts. The proposed model employs a hybrid DL network that can recognize Arabic printed text without the need for character segmentation. The model was tested on a custom dataset comprised of over two million word samples that were generated using 18 different Arabic font types. The objective of the testing process was to assess the model capability in recognizing a diverse set of Arabic fonts representing a varied cursive styles. The model achieved good results in recognizing characters and words and it also achieved promising resu...
This work presents a comparison for the performance of sequential sorting algorithms under four d... more This work presents a comparison for the performance of sequential sorting algorithms under four different modes of execution, the sequential processing mode, a conventional multi-threading implementation, multi-threading with OpenMP Library and finally parallel processing on a super computer. Quick Sort algorithm was selected to run the experiments performed by this effort and the algorithm was run using different arrays sizes and different number of processors. The results and findings were analyzed uncovering limitations as well as enhancement potentials of sequential sorting algorithms using parallelism.
ArXiv, 2021
This work explores the characteristics of implementing parallel Quick Sort algorithm over the OTI... more This work explores the characteristics of implementing parallel Quick Sort algorithm over the OTIS Hyper Hexa-Cell interconnection network OHHC. OHHC interconnection architecture offers efficient processor connectivity by utilizing both electronic and optical based connections. The work presented includes analytical evaluation of the algorithm as well as simulated evaluation over a multi-threading environment. Different experiments were performed using different OHHC dimensions, different integer array types and different array sizes. The evaluation and simulation demonstrated encouraging results that proposes the OHHC connectivity networks as a promising architecture.
ArXiv, 2021
This work presents a comparison for the performance of sequential sorting algorithms under four d... more This work presents a comparison for the performance of sequential sorting algorithms under four different modes of execution, the sequential processing mode, a conventional multi-threading implementation, multi-threading with OpenMP Library and finally parallel processing on a super computer. Quick Sort algorithm was selected to run the experiments performed by this effort and the algorithm was run using different arrays sizes and different number of processors. The results and findings were analyzed uncovering limitations as well as enhancement potentials of sequential sorting algorithms using parallelism.
International Journal of Advanced Computer Science and Applications
This paper presents an Arabic-compliant part-ofspeech (POS) tagging scheme based on using atomic ... more This paper presents an Arabic-compliant part-ofspeech (POS) tagging scheme based on using atomic tag markers that are grouped together using brackets. This scheme promotes the speedy production of annotations while preserving the richness of resultant annotations. The proposed scheme is comprised of two main elements, a new tokenization approach and a custom tool that enables the semi-automatic implementation of this scheme. The proposed model can serve in many scenarios where the user is in a need for better Arabic support and more control over the Part-of-Speech tagging process. This scheme was used to annotate sample narratives and it demonstrated capability and adaptability while addressing the various distinguishing features of Arabic language including its unique declension system. It also sets new baselines that are prospect for further exploration by future efforts.
International Journal of Advanced Computer Science and Applications
Arabic text recognition is a challenging task because of the cursive nature of Arabic writing sys... more Arabic text recognition is a challenging task because of the cursive nature of Arabic writing system, its joint writing scheme, the large number of ligatures and many other challenges. Deep Learning (DL) models achieved significant progress in numerous domains including computer vision and sequence modelling. This paper presents a model that can recognize Arabic text that was printed using multiple font types including fonts that mimic Arabic handwritten scripts. The proposed model employs a hybrid DL network that can recognize Arabic printed text without the need for character segmentation. The model was tested on a custom dataset comprised of over two million word samples that were generated using (18) different Arabic font types. The objective of the testing process was to assess the model's capability in recognizing a diverse set of Arabic fonts representing a varied cursive styles. The model achieved good results in recognizing characters and words and it also achieved promising results in recognizing characters when it was tested on unseen data. The prepared model, the custom datasets and the toolkit for generating similar datasets are made publically available, these tools can be used to prepare models for recognizing other font types as well as to further extend and enhance the performance of the proposed model.
ArXiv, 2020
Arabic text recognition is a challenging task because of the cursive nature of Arabic writing sys... more Arabic text recognition is a challenging task because of the cursive nature of Arabic writing system, its joint writing scheme, the large number of ligatures and many other challenges. Deep Learning DL models achieved significant progress in numerous domains including computer vision and sequence modelling. This paper presents a model that can recognize Arabic text that was printed using multiple font types including fonts that mimic Arabic handwritten scripts. The proposed model employs a hybrid DL network that can recognize Arabic printed text without the need for character segmentation. The model was tested on a custom dataset comprised of over two million word samples that were generated using 18 different Arabic font types. The objective of the testing process was to assess the model capability in recognizing a diverse set of Arabic fonts representing a varied cursive styles. The model achieved good results in recognizing characters and words and it also achieved promising resu...
This work presents a comparison for the performance of sequential sorting algorithms under four d... more This work presents a comparison for the performance of sequential sorting algorithms under four different modes of execution, the sequential processing mode, a conventional multi-threading implementation, multi-threading with OpenMP Library and finally parallel processing on a super computer. Quick Sort algorithm was selected to run the experiments performed by this effort and the algorithm was run using different arrays sizes and different number of processors. The results and findings were analyzed uncovering limitations as well as enhancement potentials of sequential sorting algorithms using parallelism.
ArXiv, 2021
This work explores the characteristics of implementing parallel Quick Sort algorithm over the OTI... more This work explores the characteristics of implementing parallel Quick Sort algorithm over the OTIS Hyper Hexa-Cell interconnection network OHHC. OHHC interconnection architecture offers efficient processor connectivity by utilizing both electronic and optical based connections. The work presented includes analytical evaluation of the algorithm as well as simulated evaluation over a multi-threading environment. Different experiments were performed using different OHHC dimensions, different integer array types and different array sizes. The evaluation and simulation demonstrated encouraging results that proposes the OHHC connectivity networks as a promising architecture.
ArXiv, 2021
This work presents a comparison for the performance of sequential sorting algorithms under four d... more This work presents a comparison for the performance of sequential sorting algorithms under four different modes of execution, the sequential processing mode, a conventional multi-threading implementation, multi-threading with OpenMP Library and finally parallel processing on a super computer. Quick Sort algorithm was selected to run the experiments performed by this effort and the algorithm was run using different arrays sizes and different number of processors. The results and findings were analyzed uncovering limitations as well as enhancement potentials of sequential sorting algorithms using parallelism.
International Journal of Advanced Computer Science and Applications
This paper presents an Arabic-compliant part-ofspeech (POS) tagging scheme based on using atomic ... more This paper presents an Arabic-compliant part-ofspeech (POS) tagging scheme based on using atomic tag markers that are grouped together using brackets. This scheme promotes the speedy production of annotations while preserving the richness of resultant annotations. The proposed scheme is comprised of two main elements, a new tokenization approach and a custom tool that enables the semi-automatic implementation of this scheme. The proposed model can serve in many scenarios where the user is in a need for better Arabic support and more control over the Part-of-Speech tagging process. This scheme was used to annotate sample narratives and it demonstrated capability and adaptability while addressing the various distinguishing features of Arabic language including its unique declension system. It also sets new baselines that are prospect for further exploration by future efforts.
International Journal of Advanced Computer Science and Applications
Arabic text recognition is a challenging task because of the cursive nature of Arabic writing sys... more Arabic text recognition is a challenging task because of the cursive nature of Arabic writing system, its joint writing scheme, the large number of ligatures and many other challenges. Deep Learning (DL) models achieved significant progress in numerous domains including computer vision and sequence modelling. This paper presents a model that can recognize Arabic text that was printed using multiple font types including fonts that mimic Arabic handwritten scripts. The proposed model employs a hybrid DL network that can recognize Arabic printed text without the need for character segmentation. The model was tested on a custom dataset comprised of over two million word samples that were generated using (18) different Arabic font types. The objective of the testing process was to assess the model's capability in recognizing a diverse set of Arabic fonts representing a varied cursive styles. The model achieved good results in recognizing characters and words and it also achieved promising results in recognizing characters when it was tested on unseen data. The prepared model, the custom datasets and the toolkit for generating similar datasets are made publically available, these tools can be used to prepare models for recognizing other font types as well as to further extend and enhance the performance of the proposed model.