Randa Elanwar
I work as a researcher at the Electronics Research Institute (Egyptian research center). I received my Bachelor degree with honors from Cairo University in 2003, Masters degree in 2007, and PhD degree in 2012 from Cairo University (Electronics & Communications Eng.).
My research interests are in digital signal processing, computer vision and machine learning applications regarding Document Analysis, Handwriting recognition systems and Information retrieval
I have good experience in problem solving, management, planning, critical thinking, research tools and error analysis, thus I'm open to provide technical consultancy in research projects besides scientific research.
I'm in continuous learning of programming languages, machine learning tools, and project management techniques. I like to read about new technologies to know how things work.
I appreciate team work, though capable of working independently and like to have a positive environment in the workplace. I'm self motivated, I like challenges and get prepared for risks.
My best skills include conducting research, technical writing, translation, strategic planning, and data analysis
I volunteer with professional skills so I'm always seeking opportunities to spread and exchange knowledge via training or teaching.
I support several causes with education on the top of them and believe that technology has to have an effect in making people lives better especially those who are under-served. I support knowledge sharing in Arabic and raising awareness beyond the barriers of languages so I write articles in Arabic and translate to Arabic as well as directing my postdoctoral research application to Arabic because it's less addressed and more challenging.
I support simplifying science and motivating learners so I'm always finding my way through training postgraduate students, university teaching and article writing.
Supervisors: Mohsen Rashwan and Samia Mashali
My research interests are in digital signal processing, computer vision and machine learning applications regarding Document Analysis, Handwriting recognition systems and Information retrieval
I have good experience in problem solving, management, planning, critical thinking, research tools and error analysis, thus I'm open to provide technical consultancy in research projects besides scientific research.
I'm in continuous learning of programming languages, machine learning tools, and project management techniques. I like to read about new technologies to know how things work.
I appreciate team work, though capable of working independently and like to have a positive environment in the workplace. I'm self motivated, I like challenges and get prepared for risks.
My best skills include conducting research, technical writing, translation, strategic planning, and data analysis
I volunteer with professional skills so I'm always seeking opportunities to spread and exchange knowledge via training or teaching.
I support several causes with education on the top of them and believe that technology has to have an effect in making people lives better especially those who are under-served. I support knowledge sharing in Arabic and raising awareness beyond the barriers of languages so I write articles in Arabic and translate to Arabic as well as directing my postdoctoral research application to Arabic because it's less addressed and more challenging.
I support simplifying science and motivating learners so I'm always finding my way through training postgraduate students, university teaching and article writing.
Supervisors: Mohsen Rashwan and Samia Mashali
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focused on analyzing layouts for Arabic scanned book pages (SAB). PLA-SAB required solutions of two tasks: page-to-block segmentation and block text/non-text classification. In this paper we briefly describe the methods provided by participating teams, present their results for both tasks using the BCEArabic benchmarking dataset [1], and make an open call for continuous participation outside the context of ASAR 2018.
BCE-Arabic-v1 [22]. Our system shows strong performance on BCE data in terms of CC classification accuracy and F1-score (97.5% and 97.7% respectively). When evaluated on datasets by other researchers [2], [11], RFAAD also performs well. Moreover, RFAAD shows moderately strong performance when applied to the most challenging layouts of the benchmarking dataset of the ASAR 2018 competition PLA-SAB.1 The
performance of RFAAD suggests that our work, with some modifications, has the potential to solve other open problems in the document analysis area and attain a relatively high degree of generalization.
machines for conducting a logical Layout Analysis of scanned pages of Books in Arabic. Our system labels the function (class) of a document(scanned book pages) region, based on its position on the page and other features. We evaluated LABA with the benchmark ”BCE-Arabic-v1” dataset, which contains scanned pages of illustrated Arabic books. We obtained high recall and precision values, and found that the F-measure of LABA is higher for all classes except the ”noise” class compared to a neural network method that was based on prior work.
impairments that make it difficult for them to access
printed text. Assistive technologies such as scanners and
screen readers often fail to turn text into speech because
optical character recognition software (OCR) has difficulty
to interpret the textual content of Arabic documents. In
this paper, we show that the inaccessibility of scanned PDF
documents is in large part due to the failure of the OCR engine
to understand the layout of an Arabic document. Arabic
document layout analysis (DLA) is therefore an urgent
research topic, motivated by the goal to provide assistive
technology that serves people with visual impairments. We
announce the launching of a large annotated dataset of Arabic
document images, called BCE-Arabic-v1, to be used as
a benchmark for DLA, OCR and text-to-speech research.
Our dataset contains 1,833 images of pages scanned from
180 books and represents a variety of page content and layout,
in particular, Arabic text in various fonts and sizes,
photographs, tables, diagrams, and charts in single or multiple
columns. We report the results of a formative study
that investigated the performance of state-of-the-art document
annotation tools. We found significant differences and
limitations in the functionality and labeling speed of these
tools, and selected the best-performing tool for annotating
our benchmark BCE-Arabic-v1.
focused on analyzing layouts for Arabic scanned book pages (SAB). PLA-SAB required solutions of two tasks: page-to-block segmentation and block text/non-text classification. In this paper we briefly describe the methods provided by participating teams, present their results for both tasks using the BCEArabic benchmarking dataset [1], and make an open call for continuous participation outside the context of ASAR 2018.
BCE-Arabic-v1 [22]. Our system shows strong performance on BCE data in terms of CC classification accuracy and F1-score (97.5% and 97.7% respectively). When evaluated on datasets by other researchers [2], [11], RFAAD also performs well. Moreover, RFAAD shows moderately strong performance when applied to the most challenging layouts of the benchmarking dataset of the ASAR 2018 competition PLA-SAB.1 The
performance of RFAAD suggests that our work, with some modifications, has the potential to solve other open problems in the document analysis area and attain a relatively high degree of generalization.
machines for conducting a logical Layout Analysis of scanned pages of Books in Arabic. Our system labels the function (class) of a document(scanned book pages) region, based on its position on the page and other features. We evaluated LABA with the benchmark ”BCE-Arabic-v1” dataset, which contains scanned pages of illustrated Arabic books. We obtained high recall and precision values, and found that the F-measure of LABA is higher for all classes except the ”noise” class compared to a neural network method that was based on prior work.
impairments that make it difficult for them to access
printed text. Assistive technologies such as scanners and
screen readers often fail to turn text into speech because
optical character recognition software (OCR) has difficulty
to interpret the textual content of Arabic documents. In
this paper, we show that the inaccessibility of scanned PDF
documents is in large part due to the failure of the OCR engine
to understand the layout of an Arabic document. Arabic
document layout analysis (DLA) is therefore an urgent
research topic, motivated by the goal to provide assistive
technology that serves people with visual impairments. We
announce the launching of a large annotated dataset of Arabic
document images, called BCE-Arabic-v1, to be used as
a benchmark for DLA, OCR and text-to-speech research.
Our dataset contains 1,833 images of pages scanned from
180 books and represents a variety of page content and layout,
in particular, Arabic text in various fonts and sizes,
photographs, tables, diagrams, and charts in single or multiple
columns. We report the results of a formative study
that investigated the performance of state-of-the-art document
annotation tools. We found significant differences and
limitations in the functionality and labeling speed of these
tools, and selected the best-performing tool for annotating
our benchmark BCE-Arabic-v1.