Search Results (4)

Urdu is a complex language as it is an amalgam of many South Asian and East Asian languages; hence, its character recognition is a huge and difficult task. It is a bidirectional language with its numerals written from left to right while script is written in opposite direction which induces complexities in the recognition process. This paper presents the recognition and classification of a novel Urdu numeral dataset using convolutional neural network (CNN) and its variants. We propose custom CNN model to extract features which are used by Softmax activation function and support vector machine (SVM) classifier. We compare it with GoogLeNet and the residual network (ResNet) in terms of performance. Our proposed CNN gives an accuracy of 98.41% with the Softmax classifier and 99.0% with the SVM classifier. For GoogLeNet, we achieve an accuracy of 95.61% and 96.4% on ResNet. Moreover, we develop datasets for handwritten Urdu numbers and numbers of Pakistani currency to incorporate real-life problems. Our models achieve best accuracies as compared to previous models in the literature for optical character recognition (OCR). Full article

In recent years, numerous changes have emerged in the History of Science of what has traditionally been called the Islamic world. By now, it has become usual to speak of the Islamicate world, albeit more so in Islamic Studies and related historical disciplines. The notion Islamicate wishes to express that the societies rule by Muslim dynasties were multi-cultural, multi-ethnic, multi-confessional and plurilingual. Different Muslim denominations could form majority but also minority groups. The processes of change in the study of the sciences in those societies can be summarized as efforts to pluralize research approaches and to historicize objects, themes, people, institutions and practices. The pluralization of approaches includes the multiplication of (1) modern disciplinary homes for studies of scientific topics dealt with in Islamicate societies, (2) the languages acknowledged as languages of scientific texts such as New Persian, Ottoman Turkish or Urdu worthwhile to analyze, (3) the number of historical disciplines accepted under the umbrella of history of science, (4) the centuries or periods as well as the regions that have been incorporated into the investigation of past scientific knowledge and (5) the recognition that more than a single history can and should be told about the sciences in past Islamicate societies. The process of historicization means, first and foremost, to turn away from macro-units of research (Islam, medieval or Arabic science) to medium- or micro-level units. Historicization indicates, secondly, the turn toward contextualization beyond the analysis of individual texts or instruments. And thirdly, it signifies the integration of features or aspects of scholarly practices that are not limited to the content of a discipline or a text but include layouts, the organization of text production, types of visualizations of knowledge or rhetorical strategies and paratextual elements. My paper reports on trends that I consider relevant for understanding how the field changed over the last decades and how it ticks today. But it does not try to be comprehensive. Full article

Named entity recognition (NER) is an important task in natural language processing, as it is widely featured as a key information extraction sub-task with numerous application areas. A plethora of attempts was made for NER detection in Western and Asian languages. However, little effort has been made to develop techniques for the Urdu language, which is a prominent South Asian language with hundreds of millions of speakers across the globe. NER in Urdu is considered a hard problem owing to several reasons, including the paucity of large, annotated datasets; an inaccurate tokenizer; and the absence of capitalization in the Urdu language. To this end, this study proposed a conditional-random-field-based technique with both language-dependent and language-independent features, such as part-of-speech tags and context windows of words, respectively. As a second contribution, we developed an Urdu NER dataset (UNER-I) in which a large number of NE types were manually annotated. To evaluate the effectiveness of the proposed approach, as well as the usefulness of the dataset, experiments were performed using the dataset we developed and an existing dataset. The results of the experiments showed that our proposed technique outperformed the baseline technique for both datasets by improving the F1 scores by 1.5% to 3%. Furthermore, the results demonstrated that the enhanced dataset was useful for learning and prediction in a supervised learning approach. Full article

The ongoing development of audio datasets for numerous languages has spurred research activities towards designing smart speech recognition systems. A typical speech recognition system can be applied in many emerging applications, such as smartphone dialing, airline reservations, and automatic wheelchairs, among others. Urdu is a national language of Pakistan and is also widely spoken in many other South Asian countries (e.g., India, Afghanistan). Therefore, we present a comprehensive dataset of spoken Urdu digits ranging from 0 to 9. Our dataset has 25,518 sound samples that are collected from 740 participants. To test the proposed dataset, we apply different existing classification algorithms on the datasets including Support Vector Machine (SVM), Multilayer Perceptron (MLP), and flavors of the EfficientNet. These algorithms serve as a baseline. Furthermore, we propose a convolutional neural network (CNN) for audio digit classification. We conduct the experiment using these networks, and the results show that the proposed CNN is efficient and outperforms the baseline algorithms in terms of classification accuracy. Full article