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Sheikh Abujar

Bio: Sheikh Abujar is an academic researcher from Daffodil International University. The author has contributed to research in topics: Bengali & Automatic summarization. The author has an hindex of 9, co-authored 67 publications receiving 268 citations. Previous affiliations of Sheikh Abujar include Independence University & Jahangirnagar University.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: A simple, lightweight CNN model has been proposed in this paper for classifying Bangla Handwriting Character, which contains 50 basic Bangla characters, and achieved the best accuracy rate so far for BanglaLekha-Isolated, CMATERdb and ISI datasets.

53 citations

Journal ArticleDOI
TL;DR: EkushNet is the first research which can recognize Bangla handwritten basic characters, digits, modifiers, and compound characters, which is so far, the best accuracy for Bangla character recognition.

40 citations

Journal ArticleDOI
TL;DR: This article proposed a artificial Bangla Text Generator with LSTM, which is very early for this language and also this model is validated with satisfactory accuracy rate.

37 citations

Book ChapterDOI
21 Dec 2018
TL;DR: A multipurpose comprehensive dataset for Bangla Handwritten Characters that contains Bangla modifiers, vowels, consonants, compound letters and numerical digits to fabricate acknowledgment technique for written Bangla characters.
Abstract: Ekush the largest dataset of handwritten Bangla characters for research on handwritten Bangla character recognition. In recent years Machine learning and deep learning application-based researchers have achieved interest and one of the most significant application is handwritten recognition. Because it has the tremendous application such in Bangla OCR. Also, Bangla writing script is one of the most popular in the world. For that reason, we are introducing a multipurpose comprehensive dataset for Bangla Handwritten Characters. The proposed dataset contains Bangla modifiers, vowels, consonants, compound letters and numerical digits that consists of 367,018 isolated handwritten characters written by 3086 unique writers which were collected within Bangladesh. This dataset can be used for other problems i.e.: gender, age, district base handwritten related research, because the samples were collected include verity of the district, age group and the equal number of male and female. It is intended to fabricate acknowledgment technique for hadn written Bangla characters. This dataset is unreservedly accessible for any sort of scholarly research work. The Ekush dataset is trained and validated with EkushNet and indicated attractive acknowledgment precision 97.73% for Ekush dataset, which is up until this point, the best exactness for Bangla character acknowledgment. The Ekush dataset and relevant code can be found at this link: https://github.com/ShahariarRabby/ekush.

36 citations

Proceedings ArticleDOI
06 Jul 2019
TL;DR: The main goal was increased the efficiency and reduce train loss of sequence to sequence model for making a better abstractive text summarizer and successfully reduced the training loss with a value of 0.036.
Abstract: Text summarization is one of the famous problems in natural language processing and deep learning in recent years. Generally, text summarization contains a short note on a large text document. Our main purpose is to create a short, fluent and understandable abstractive summary of a text document. For making a good summarizer we have used amazon fine food reviews dataset, which is available on Kaggle. We have used reviews text descriptions as our input data, and generated a simple summary of that review descriptions as our output. To assist produce some extensive summary, we have used a bi-directional RNN with LSTM's in encoding layer and attention model in decoding layer. And we applied the sequence to sequence model to generate a short summary of food descriptions. There are some challenges when we working with abstractive text summarizer such as text processing, vocabulary counting, missing word counting, word embedding, the efficiency of the model or reduce value of loss and response machine fluent summary. In this paper, the main goal was increased the efficiency and reduce train loss of sequence to sequence model for making a better abstractive text summarizer. In our experiment, we've successfully reduced the training loss with a value of 0.036 and our abstractive text summarizer able to create a short summary of English to English text.

27 citations


Cited by
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Journal ArticleDOI
TL;DR: Two different deep architectures for detecting the type of infection in tomato leaves are presented and the first architecture applies residual learning to learn significant features for classification and the second architecture applies attention mechanism on top of the residual deep network.

228 citations

Journal ArticleDOI
TL;DR: This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.
Abstract: Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence/machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

139 citations

Posted Content
TL;DR: In this paper, a systematic literature review (SLR) is presented to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions, which serve the purpose of presenting state of the art results and techniques on OCR.
Abstract: Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2018. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 142 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

93 citations

Journal ArticleDOI
Bo Jiang1, Qian Wu1, Xuqiang Yin1, Dihua Wu1, Huaibo Song1, Dongjian He1 
TL;DR: A deep learning network named FLYOLOv3 (FilterLayer YOLOV3) based on FilterLayer was tested to achieve the detection of key parts of dairy cows in complex scenes and showed that the proposed algorithm could be used for high-precision detection of the key partsof dairy cows.

67 citations

Journal ArticleDOI
TL;DR: This study aims to present an overview of approaches that can be applied to extract and later present these valuable information nuggets residing within text in brief, clear and concise way.
Abstract: With the advent of Web 2.0, there exist many online platforms that results in massive textual data production such as social networks, online blogs, magazines etc. This textual data carries information that can be used for betterment of humanity. Hence, there is a dire need to extract potential information out of it. This study aims to present an overview of approaches that can be applied to extract and later present these valuable information nuggets residing within text in brief, clear and concise way. In this regard, two major tasks of automatic keyword extraction and text summarization are being reviewed. To compile the literature, scientific articles were collected using major digital computing research repositories. In the light of acquired literature, survey study covers early approaches up to all the way till recent advancements using machine learning solutions. Survey findings conclude that annotated benchmark datasets for various textual data-generators such as twitter and social forms are not available. This scarcity of dataset has resulted into relatively less progress in many domains. Also, applications of deep learning techniques for the task of automatic keyword extraction are relatively unaddressed. Hence, impact of various deep architectures stands as an open research direction. For text summarization task, deep learning techniques are applied after advent of word vectors, and are currently governing state-of-the-art for abstractive summarization. Currently, one of the major challenges in these tasks is semantic aware evaluation of generated results.

65 citations