In the age of start-ups and technical research, the demand for high-end computing power and loads of space is ever increasing. Machine learning techniques have become an inseparable part of the big data analytics. Setting up one’s own infrastructure to deal with all this vastness is usually not feasible due to high expenses and lack of desired expertise. As a solution to this problem, this paper proposes a system for Big-Data Analytics and Machine Learning based on Hadoop and Spark frameworks that also supports Operating System (OS) Rental Services. Machine Learning (ML) services provide option to use both existing inbuilt popular models or create one’s own model. OS Rental services provide users with high end infrastructure on their low-end devices on rent. The entire implementation has been made open source for ease of access and facilitating extensibility.

In this paper, a vehicle number plate recognition (VNPR) system is implemented for Indian vehicles. For this purpose, we propose a 'region-of-interest (ROI)'-based filtering method to locate the candidate regions of number plate (NP) occurrence. In the proposed filtering method, candidate regions are located in the NP image by detecting vertical edges, removing long edges and stationary regions. Finally, the NP region is segmented from the candidate regions before passing it to the optical character recognition (OCR) system for recognition of characters and digits present in the number plate. The novelty of the proposed VNPR system lies in exploring the ROI-based filtering method which improves the overall performance of the proposed VNPR system. The proposed system has been tested using various NP images of vehicles extracted from real-life video sequences that vary along the dimensions of light, scale and orientation. The experimental results demonstrate the robustness of the proposed method.