I-Fintech adoption readinesss

Various studies show information technology can beneficially apply to enhance financial services. The merger of Fintech, based on the Islamic law, creates the concept of Islamic Fintech (I-Fintech). The growing investment and numerous startups across Islamic Fintech offer financial innovation evidence of the importance of I-Fintech. Many governments support I-Fintech growth through investments and supporting startups. Pakistan, an Islamic country, is experiencing evolutionary changes in Fintech adoption. This I-Fintech adoption can engage governments, companies and firms across the Islamic world. Eighty five percent (85%) of Pakistan’s population lack financial services. Hence, financial inclusion is a dominant problem of Pakistan. To date, few I-Fintech companies or firms operate in Pakistan. These are limited to big cities like Islamabad, Karachi and Lahore. To date lack of investment has restricted I-Fintech growth processes. Thus, a demand for local corporate and firm engagement is desirable to capture full advantages across the Fintech sector. This paper proposes a conceptual framework for adoption of I-Fintech across Pakistan. It proposes Islamic Fintech challenges and risk affect intention to adopt I-Fintech in Pakistan. Intention to adopt I-Fintech technology contains the constructs of technical literacy, financial literacy, digital literacy and social acceptance. Tis study’s next stage is to measure and model competitiveness position of Pakistan’s Islamic financial institutions (by gauging their resultant collective intelligences position)

Named Entity Recognition for Urdu Language: The UNER System, A Hybrid Approach

NER is a natural language processing technique that primarily classifies parts of parsed text into well-known named entities. In the domain of natural language processing, the recognition of name entities is used to classify nouns that appear in bulk text data and place these nouns into predefined groups, such as names of people, places, times, dates, organizations, etc. There is a lot of fragmented material and data on the Cyberspace, therefore scholars are working on several languages (i.e: Sindhi, English, etc.), by working on various approaches and techniques depending on their locations, to improve accessibility of filtered information for online users. The NER enhance the quality of NLP in applications including automated summarization, semantic web search, information extraction and retrieval machine translation and question answering, chatbots and others. This study designs an efficient framework to extract noun entities in Urdu using a hybrid approach. The UNER system not only extracts entities by searching through a list of names, but also extracts named entities by recognizing phrases in a given text. The UNER system is designed to recognize Urdu noun entities in pre-defined categories such as places, personal names, titled personal names, organizations, object names, trade names, abbreviations, dates and times, measurements, and text names in Urdu

Issues & Challenges in Urdu OCR

Optical character recognition is a technique that is used to recognized printed and handwritten text into editable text format. There has been a lot of work done through this technology in identifying characters of different languages with variety of scripts. In which Latin scripts with isolated characters (non-cursive) like English are easy to recognize and significant advances have been made in the recognition; whereas, Arabic and its related cursive languages like Urdu have more complicated and intermingled scripts, are not much worked. This paper discusses a detail of various scripts of Urdu language also discuss issues and challenges regarding Urdu OCR. due to its cursive nature which include cursiveness, more characters dots, large set of characters for recognition, more base shape group characters, placement of dots, ambiguity between the characters and ligatures with very slight difference, context sensitive shapes, ligatures, noise, skew and fonts in Urdu OCR. This paper provides a better understanding toward all the possible engendering dilemmas related to Urdu character recognition