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Work on the problem of handwritten text recognition in Urdu script has been an active research area. A significant progress is made in this interesting and challenging field in the last few years. In this study, the authors presented a comprehensive survey for a number of offline and online handwritten text recognition systems for Urdu script written in Nastaliq font style from 2004 to 2019. Following features make their contribution worthwhile and unique among the reviews of a similar kind: (i) their review classifies the existing studies based on types of recognition systems used for Urdu handwritten text, (ii) it covers a very different outlook of the recognition process of the Urdu handwritten text at different granularity levels (e.g. character, word, ligature, or sentence level), (iii) this review article also presents each of surveyed articles in following dimensions: the task performed, its granularity level, dataset used, results obtained, and future dimensions, and (iv) lastly it gives the summary of the surveyed articles according to the granularity levels, publishing years, related tasks or subtasks, and types of classifiers used. In the end, major challenges and tasks related to Urdu handwritten text recognition approaches are also discussed in detail.

Our study is a scientometric analysis of research publications of science and social science disciplines in Pakistan during 2009–2018. The study examines 2000 published articles belonging to 50 research scholars of different disciplines. This analysis is conducted on three different levels: researcher level, field level and domain level. In this paper we discuss readability scores, title formats, single and multiple authorships of articles, citation rates, publication rates over time, the research contribution of both genders and the impact of PhD institution of authors in research publications. The IR features like number of words, sentence length and readability scores are extracted. The results indicate that more research is being conducted in science disciplines as compared to social sciences. It also appears that the readability scores of authors changed over time, and Science articles have higher readability scores compared to Social Science articles. Moreover, title formats are observed to effect citation rates. Titles tend to use more colons over time; and the use of simple sentences as titles is more than 50%, while use of question marks is below 3%, the rest are titles with colons. The citation rates also appear to increase due to co-authorship. Male researchers contribute more to research activities in science disciplines while in social science the ratio is almost equal while PhD institutes appear to influence the performance of researcher slightly. Finally, it is observed that the publication rates of Science disciplines are much higher than Social Science articles, depicting that the growth of Science disciplines is more rapid in Pakistan.

In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.