Regional and cultural diversities around the world have given birth to a large number of writing systems and scripts, which consist of varying character sets. Developing an optimal OCR for such a varying and large character set is a challenging task. Unlimited variations in handwritten text due to mood swings, varying writing styles, changes in medium of writing, and many more puzzle the research community. To overcome this problem, researchers have proposed various techniques for the automatic recognition of cursive languages like Urdu, Pashto, and Arabic. With the passage of time, the field of text recognition matured, and the number of publications exponentially increased in the targeted field. It is very difficult to find all the techniques developed, calculate the time and resource consumptions, and understand the cost-benefit tradeoffs among these techniques. These tradeoffs resist making this technology able for practical use. To address these tradeoffs, this paper systematic analysis to identify gaps in the literature and suggest new enhanced solution accordingly. A total of 153 of the most relevant articles are analyzed in this SLR work aired from 2008 – 2022. This systematic review process shows: (1) the list of techniques suggested for cursive text recognition purposes and its capabilities, (2) set of feature extraction techniques proposed, and (3) implementation tools used to design and simulate the empirical studies in this specialized field. We have also discussed the emerging trends and described their implications for the research community in this specialized domain. This systematic assessment will ultimately help the researchers to perform an overview of the existing character/text recognition approaches, recognition capabilities, time consumption, and subsequently identify the areas that requires a significant attention in the near future.
