ڈاکٹر ڈی ، اے ، اسمتھ
ماہ گذشتہ میں آکسفرڈ سے ڈاکٹر ڈی، اے، اسمتھ، ال، ڈی کی وفات کی خبر موصول ہوئی ۔ ڈاکٹر موصوف مشرقی علوم سے خاص شغف رکھتے تھے، اور تاریخ ہند کے ایک مستند عالم سمجھے جاتے تھے۔ تقریباً ۱۸۷۰ء میں وہ ہندوستان میں ایک سویلین کی حیثیت سے آئے تھے، اور صوبہ متحدہ کے مختلف اضلاع میں مختلف مناصب پر فائز رہ کر کوئی بیس سال ہوئے پنشن لے کر وطن واپس گئے۔ ہندوستان کے سی (۳۰) سالہ قیام میں وہ نادر سکہ جات، کتبات وغیرہ بیش بہا تاریخی مواد فراہم کرتے رہے اور ملازمت سے سبکدوش ہونے کے بعد انھوں نے تاریخ ہند پر متعدد تصانیف شایع کیں، مثلاً اکبر اعظم، راجہ اشوک کا دورِ حکومت، وغیرہ جن میں سے بعض کتابیں ہندوستانی یونیورسٹیوں کے اعلیٰ نصابِ درس میں بھی داخل ہیں، ان کی آخری ضخیم تصنیف ’’آکسفرڈ ہسٹری آف انڈیاــ‘‘ حال ہی میں شایع ہوئی تھی۔ رایل ایشیاٹک سوسائٹی نے تمغوں اور دیگر اعزازات سے ان کی علمی خدمات کا بار بار اعتراف کیا تھا۔ (مارچ ۱۹۲۰ء)
The political reality of many countries in the Muslim World is untenable and reforms and change is absolutely necessary. This article argues that use of force and violence for political change is making things worse as one can witness in Syria and Iraq. The article advances a Qur’ānic perspective on the desirability of peace as a goal and peaceful means as instruments of change. The article acknowledges that the Qur’ānic sanction for use of force to defend religious freedom prcludes the advocacy of pacifism but nevertheless the article does make a strong case for privileging peace over use of force.
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.