Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Text Extraction from Bills and Invoices

Abstract: This research tries to find out a methodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on – such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices.

Comparative analysis of nature inspired algorithms on data clustering

Abstract: K-Means clustering is well accepted clustering algorithm that huddle similar data objects in a simple and quick way. The convergence speed of K-Means clustering is quite appreciable but it has drawback of getting stuck into local optima. Hence, optimal clustering results are not attained. Nature inspired algorithm when integrated with clustering algorithm provides global optimal solution. The paper analyzes three nature inspired algorithms i.e. firefly algorithm, bat algorithm, and flower pollination algorithm integrated with K-Means clustering. The study is performed on four real life datasets obtained from UCI machine learning repository and two simulated datasets. Algorithms are evaluated on the basis of number of fitness function and CPU time per run. It is observed from experimental study that integrated flower pollination algorithm with K-Means overrule the other two algorithm on each datasets.