Urdu Nastaleeq Nib Calligraphy Pattern Recognition
Mateen Ahmed Abbasi,
Naila Fareen,
Adnan Ahmed Abbasi
Issue:
Volume 6, Issue 2, December 2020
Pages:
16-21
Received:
31 March 2020
Accepted:
10 April 2020
Published:
27 August 2020
Abstract: Nib calligraphy pattern recognition is the way to convert handwritten nib font into its equivalent machine understandable or readable form. Nib calligraphy pattern recognition is derived from pattern recognition and computer vision, a variety of work has been done on Urdu literature and on Urdu handwritten automatic line segmentation. This research work is based on Urdu Nastaleeq Nib calligraphy pattern recognition. The width of the Qalam (Nib) makes difficulties in recognition due to different width of qalam pattern varieties, so there is dire need to develop a system that can recognize the digitized image of Urdu Nastaleeq Nib font with high accuracy. The objective of this research is to create a ground for the development of an efficient and robust Urdu Optical Character Recognition (OCR) for Urdu Nastaleeq nib pattern recognition and to develop a system that can recognize the digitized image of Urdu Nastaleeq Nib font with high accuracy. Urdu Nastaleeq nib pattern recognition. The research work mainly focuses on identifying the Urdu nib calligraphy pattern recognition. The purpose of the research is to create a system for Urdu Nastaleeq Nib calligraphy pattern recognition to get benefit from the cultural heritage of Nib calligraphic material. The Urdu Nastaleeq Nib Calligraphy Pattern Recognition research work is proposed to be done on the calligraphic Urdu Nastaleeq Nib pattern recognition. This research mainly focuses on recognizing the handwritten Urdu Nastaleeq Nib typeset and eliminating the noise which is the main difficulty in interpretation the font clearly. The aim here is to build up a more consistent, correct and precise system for Urdu Nastaleeq Nib calligraphy Pattern Recognition.
Abstract: Nib calligraphy pattern recognition is the way to convert handwritten nib font into its equivalent machine understandable or readable form. Nib calligraphy pattern recognition is derived from pattern recognition and computer vision, a variety of work has been done on Urdu literature and on Urdu handwritten automatic line segmentation. This research...
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Overview of the Three-dimensional Convolutional Neural Networks Usage in Medical Computer-aided Diagnosis Systems
Issue:
Volume 6, Issue 2, December 2020
Pages:
22-28
Received:
4 August 2020
Accepted:
17 August 2020
Published:
27 August 2020
Abstract: Medical computer-aided diagnosis systems are essential applications that help doctors speed up, standardize, and improve disease prediction quality. Nevertheless, it is hard to implement a high-accuracy diagnosis system due to complex medical data structures that are hard to interpret even by an experienced radiologist, lack of the labeled data, and the high-resolution three-dimensional nature of the data. Meanwhile, modern deep learning methods achieved a significant breakthrough in various computer vision tasks. Thus, the same methods began to gain popularity in the community that works on the computer-aided systems implementation. Most modern diagnosis systems work with three-dimensional medical images that cannot be processed by traditional two-dimensional convolutional neural networks to get high enough prediction results. Hence, medical research introduced new methods that use three-dimensional neural networks to work with medical images. Even though these networks are usually an adapted version of state-of-the-art two-dimensional networks, they still have their specifics and modifications that help achieve human-level accuracy and should be considered separately. This article overviews the three-dimensional convolutional neural networks and how they are different from their two-dimensional versions. Moreover, the article examines the most influenced systems that achieve human-level accuracy in predicting the specific disease. The networks discussed in the perspective of two basic tasks: segmentation and classification. That is because the simple end-to-end classification neural networks usually do not work well on the available amount of data in the medical domain.
Abstract: Medical computer-aided diagnosis systems are essential applications that help doctors speed up, standardize, and improve disease prediction quality. Nevertheless, it is hard to implement a high-accuracy diagnosis system due to complex medical data structures that are hard to interpret even by an experienced radiologist, lack of the labeled data, an...
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A Neural Network Approach to Writer’s Model for Full Likelihood Ratio in Handwriting Analysis
Abiodun Adeyinka Oluwabusayo,
Adeyemo Adesesan Barnabas
Issue:
Volume 6, Issue 2, December 2020
Pages:
29-35
Received:
4 November 2020
Accepted:
2 December 2020
Published:
16 December 2020
Abstract: Handwriting is an integral part of our life that can predict who we are because the style of writing is unique for every person. Handwriting is also a key element in document examination as it leaves a forensic document examiner with the task of determining who the writer of a particular document is and this is achieved through the likelihood ratio (LR) paradigm. Inability to model an individual’s handwriting over time has made estimating a full likelihood ratio for comparative handwriting analysis impossible thereby employing nuisance parameters and subjectivity in computation of LR that is not full. This research employed back propagation neural network (BPNN) to model the writing pattern of individuals with input layer as the features of handwriting characters, two hidden layers of three neurons each, activation function sigmoid (s) and an output handwriting. With the help of handwriting model for individual writers, little or no assumptions and no nuisance parameters were employed in achieving full likelihood ratio for comparative handwriting analysis in forensic science. From the research carried out, it can be concluded that modeling an individual’s handwriting is a crucial factor in achieving a full likelihood ratio, little/or no inconclusiveness in result reporting and a less degree of disagreements for handwriting identification in a forensic environment.
Abstract: Handwriting is an integral part of our life that can predict who we are because the style of writing is unique for every person. Handwriting is also a key element in document examination as it leaves a forensic document examiner with the task of determining who the writer of a particular document is and this is achieved through the likelihood ratio...
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