Develop an offline handwriting recognition engine that can automatically read handwritten prescriptions in English from scanned or captured images. The perfect combination of a deep learning neural network and a deep learning neural network will make it possible to process the content of documents with even greater accuracy and clarity. Text can be extracted with high accuracy regardless of the quality of the original document, whether printed text, handwriting, or low-quality images.

Big Data Platform: Craw, collect, store, and analyse data from a variety of different data sources in real-time. Provide wide range of “bigdata reports” in real-time for the Company key stakeholders in order to help them make quick and accurate decisions to improve the business outcome. Video Recommendation System: The Recommendation System for Video is developed upon Bigdata platform/technologies. This system gives personalised recommendation for customers on Web, OTT App, and Set-top-box platforms based on the customer history, the information of genre, content, and other similar user history also giving upcoming trending for customer.

The project aims to develop an engine that can automatically detect & remove rubber stamp from scanned/captured document images.. There are many challenging that we have to cope with in this project. For example, no any standards for rubbers so far (e.g. the variety of rubber shapes, colors) or especially dealing with both scanned and captured images are also a big challenging. After trying several methods and considering between two important metrics (accuracy and performance), we finally deployed YOLOv3 for object detection step and making use of K-means scikit-learn and OpenCV for output generation.