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Online Urdu Handwritten Character Recognition System

Thesis Info

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Author

Safdar, Quara-Tul-Ain

Program

PhD

Institute

Pakistan Institute of Engineering and Applied Sciences

City

Islamabad

Province

Islamabad.

Country

Pakistan

Thesis Completing Year

2017

Thesis Completion Status

Completed

Subject

Electrical Engineering

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/11731/1/Quara%20tul%20ain%20safdar%20electrical%20engg%20year%202019%20pieas%20prr.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727800927

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This thesis presents an online handwritten character recognition system for Urdu handwriting. The main target is to recognize handwritten script inputted on the touch screen of a mobile device in particular, and other touch input devices in general. Urdu alphabets are difficult to recognize because of inherent complexities of the script. In a script, Urdu alphabets appear in full as well as in half-forms: initials, medials, and terminals. Ligatures are formed by combining two or more half-form characters. The character-set in half-forms has 108 elements. The whole character-set of 108 elements is too difficult to be classified accurately by a single classifier. In this work, a framework for development of online Urdu handwriting recognition system for smartphones has been presented. A pre-classifier is de signed to segregate the large Urdu character-set into 28 smaller subsets, based on the number of strokes in a character and the position and shape of the diacrtics. This pre-classification allows to cope with the demand of robust and accurate recognition on processors having relatively low computational power and limited memory available to mobile devices, through banks of computationally less com plex classifiers. Based on the decision of the pre-classifier, the appropriate classi fier from the bank of classifiers is loaded to the memory to achieve the recognition task. A comparison of different classifier-feature combinations is presented in this study to exhibit the features’ discrimination capability and classifiers’ recognition ability. The subsets are recognized with different machine learning algorithms such as artificial neural networks, support vector machines, deep belief networks, long short-term memory recurrent neural networks, autoencoders-support vector machines, and autoencoders-deep belief networks. These classifiers are trained with wavelet transform features, structural features, and with sensory input val ues. Maximum overall classification accuracy of 97.2% has been achieved. A large database of handwritten Urdu characters is developed and employed in this study. This database contains 10800 samples of the 108 Urdu half-form characters (100 samples of each character) acquired from 100 writers.
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سرکار دی تاہنگ

سرکار دی تاہنگ

اکھاں تھکیاں تک تک راہ او خاناں
چھیتی آ ہن دیر نہ لا او خاناں
تسان ٹر گئے بہاراں رس گیاں
سسی ستی تے مہاراں کھس گیاں
تہاڈی یاد آئی اکھاں پس گیاں
بن ساون پیاس بجھا او خاناں
اکھاں تھکیاں تک تک راہ او خاناں
تہاڈے پیار سانوں مجبور ہے کیتا
توں ٹر گیا ساتھوں چپ چپیتا
کی دساں میرے نال جو بیتا
رہے اوکھا وقت نبھا او خاناں
اکھاں تھکیاں تک تک راہ او خاناں

ابتكارات العلامة الزمخشري في علم البديع خلال أسلوب السؤال والجواب في تفسيره الكشاف

The Figures of Speech(ملع عيدبلا )is a Significant branch of Arabic Rhetoric. It has two kinds; Literal Aesthetic, تانسحملا ةيظفللا)) Semantic Aesthetic, (تانسحملا ةيظفللا). Both kinds are having a pivotal role in the miracle of Qurān. The Great Scholar of Rhetoric Al-Zamakhshari has mentioned many of its types to analyze the Qurānic Verses rhetorically in his exegesis Al-Kashāf. The Great Scholar Abd Al-Qāhir Al-Jurjāni did not approach the upper mentioned kinds, not for the reason of non-interference in The Qurānic miracles but he was always eager to derive new ideas in this particular field. As it is known that many former scholars have approached all kinds of the Figures of Speech in a wide range and Abd Al-Q┐hir Al-Jurjāni was dominated by his creative nature. In this article, it has been discussed widely the academic ambivalence surrounding Abd Al-Q┐hir Al-Jurjāni's lack of interest in the Figures of Speech among three modern scholars: Dr. Muhammad Ahmad Al-ķwfi, Dr. Muhammad Shwq┘ Zaif, Dr. Muhammad Ab┴ Mosā.

Developing a Framework for Effective IT Project Management and Best HR Practices

The existing framework of Project Management advises project managers to exercise nine knowledge areas. These are management of the project's Scope, Time, Cost, Quality, HR, Communication, Procurement, Risk and Integration. It suggests entertaining these nine knowledge areas in five processes that are initiating, planning, executing, controlling and closing the project. The knowledge on HR Management (HRM) declares fourteen functions that this study identified applicable to Project Management. The literature stresses that managing all these knowledge areas determines project's outcome. The literature further indicates that nine knowledge areas are not equal in priority and HRM is not given the needful precedence. The study perceived that it is not pragmatic for a project manager to perform the nine knowledge areas and all the applicable functions of HRM efficiently. From January 2005 to January 2008, this study discovered that in the IT industry of Islamabad — Rawalpindi, Pakistan, project managers were assigned neither all the nine knowledge areas nor all the applicable functions of HRM. The study observed that projects suffered where HRM was underestimated. Can the quality in practice of HRM make or break projects? If yes, what minimum functions of HRM should be assigned to a project manager to benefit projects? Further, how can the project manager's role for precise number of knowledge areas be defined? The study assumed that precise and well-defined role of a project manager in terms of the nine knowledge areas and HRM can make the existing framework for Project Management more adoptable. For this purpose integrating the literature and the real practices in the selected IT industry this study identified and selected five FIRM functions as independent variables (IVs) keeping project result as dependent variable (DV). The IVs include selecting right person, assigning workload, setting timelines, communication and monitoring performance. This study hypothesized that the result of specific IT/Telecom project is correlated with and regressed by the quality in the practice of the mentioned HRM functions. Utilizing a valid and reliable instrument the study collected data for a stratified sample of 70 heterogeneous IT/Telecom projects from the selected 24 IT/Telecom organizations. Employing frequency & descriptive statistics, Pearson's correlations, regression and PLS regression the analyses were conducted. All the selected IVs were found correlated with project result. Individually no 1V regressed project result but collectively they all regressed the DV. The study substantiated its hypotheses based on results of regression and PLS regression. It inferred that good quality, practice of all the selected HR functions paves success for 1T/Telecom project while their substandard practice will lead project to suffering. The conclusion of the study is applicable on the IT projects of large scope and team size with well-defined type provided all the other knowledge areas for project management are exercised with necessary equilibrium. Based on results this study declares that a project manager should perform at least these five functions of HRM. The study designed templates to help project managers performing these HR functions. The mentioned results and findings from the IT industry about the knowledge areas enabled this study reshaping the existing framework for Project Management. It contributes that project manager better be set responsible only for the management of scope, time, cost, HR and communication for projects while quality, risk and procurement for projects better be managed at organization level. Project manager should consider HRM, Communication and technology the driving tools for managing other knowledge areas.