1۔قتل عمد
کسی لوہے سے، بڑی لکڑی سے یا خیمہ وغیرہ کے بانس سے یا ایسے بھاری پتھر سے جس کےمارنے سے عام طور پر آدمی کی موت واقع ہوجاتی ہو یا کسی ایسے فعل سے جو عام طور پر اتلاف جان کا سبب بن جاتاہو ۔ اگر مارا اور موت واقع ہوگئی تو یہ قتل عمد کہلائے گا۔
There is a high human value to benefit the people, especially by a business person or an organization that contributes to society with his income. This is called "Corporate Social Responsibility" in the business term. On the other hand, considering the teachings of religion Islam, it is clear that Islam is the name of well-wishing for others. Allah Almighty motivates his believers to spend a part of their wealth for the welfare of society , and Zakat is the most prominent one these orders. This research article mentions the zakat issues related to family business. It consists of nine Zakat issues that a family business faces them. During my ongoing research on family businesses, I looked at the problems faced by family businesses and then presented their solutions. During giving solutions to these problems, strong arguments are added to prove That Islam has provided the strongest and best foundation for CSR in the shape of Zakat.
Optical Character Recognition (OCR) is one of the most investigated pattern classification problems that has received remarkable research attention for more than half a century. From the simplest systems recognizing isolated digits to end-to-end recognition systems, applications of OCRs vary from postal mail sorting to reading systems in scene images facilitating autonomous navigation or assisting the visually impaired. Despite tremendous research endeavors and availability of commercial recognition engines for many scripts, recognition of cursive scripts still remains an open and challenging research problem mainly due to the complexity of script, segmentation issues and large number of classes to recognize. Among these, Urdu makes the subject of our study. More specifically, this study investigates the recognition of printed Urdu text in Nastaliq style, the most widely employed script for Urdu text that is more complex than the Naskh style of Arabic. This work presents a holistic (segmentation-free) technique that exploits ligatures (partial words) as units of recognition. Urdu has a total of more than 26,000 unique ligatures, many of the ligatures, however, share the same main body (primary ligature) and differ only in the number and position of dots and diacritics (secondary ligatures). We exploit this idea to separately recognize the primary and secondary ligatures and later re-associate the two to recognize the complete ligature. Recognition is carried out using two techniques; the first of these is based on hand-crafted statistical features using hidden Markov models (HMMs). Features extracted using sliding windows are used to train a separate model for each ligature class. Feature sequences of the query ligature are fed to all the models and recognition is carried out through the model that reports the maximum probability. The second technique employs Convolutional Neural Networks (CNNs) to automatically extract useful feature representations from the classes and recognize the ligatures. We investigated the performance of a number of pre-trained networks using transfer learning techniques and trained our own set of networks from scratch as well. Experimental study of the system is carried out on two benchmark datasets of Urdu text, the ‘Urdu Printed Text Images’ (UPTI) database and the ‘Center of Language Engineering’ (CLE) database. A number of experimental scenarios are considered for system evaluation and the realized recognition rates are compared with state-of-the-art recognition systems for printed Urdu text. An interesting aspect of experimental study is the combination of unique ligatures in the two datasets to generate a large set of around 2800 unique primary and secondary ligatures covering a major proportion of the Urdu corpus. The system reports high classification rates (88.10% and 94.78% on CLE and UPTI query ligatures respectively) demonstrating the effectiveness of the proposed recognition techniques which can be adapted for other cursive scripts as well. The findings of this study are expected to be useful for the document recognition community in general and researchers targeting cursive scripts in particular.