The objective of this work is to convert printed text or handwritten characters recorded offline using either scanning equipment or cameras into a machine-usable text by simulating a neural network so that it would improve the process of collecting and storing data by human workers. Another goal is to provide an alternate, better and faster algorithm with higher accuracy to recognize the characters. In this context, we choose artificial neural network and make it much more tolerant to anomalies in the recorded image or data. Common optical character recognition tasks involve identifying simple edge detection and matching them with predefined patterns. In this research, characters are recognized even when noise such as inclination and skewedness presents, by training the network to look for discrepancies in data and relate them using vocabulary, grammar and common recurrences that may occur after a character. Images are also masked in multiple ways and processed individually to increase the confidence level of prediction. © 2017 IEEE.

Tapan Kumar Das

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

Asis Kumar Tripathy

Department of Information Technology

Mishra A.K.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Optical character recognition using artificial neural network

2017 International Conference on Computer Communication and Informatics (ICCCI)

Background/Objectives: With the idea of enhancing the capability and effectiveness of decision making while dealing complex problems and under uncertain environments, this article provides an insight to the leading intelligent techniques that could be employed to enrich the decision-making process. Methods/Statistical Analysis: Leading techniques like expert system, artificial neural network, fuzzy system, evolutionary computing, rough set, data mining and intelligent agent are being investigated in this review for analyzing the data so as to take a prudent decision making. Findings: However, one technique does not fit for all problem domains, hence basing on the complexity and to underlying data; it has been found as which technique could be employed for a particular problem to elicit the desired knowledge out of it. Application/Improvements: This study can be helpful for designing an intelligent decision system which can be tailored for the organizations basing on their domain and nature of data. However, a hybrid intelligent technique could be proposed which could handle the decision making process in an extremely efficient manner.

Fulltext

Indian Journal of Science and Technology

Intelligent Techniques in Decision Making: A Survey

Most of the service providers and product based companies while launching brand new products, services or releasing new versions of existent products need to campaign to reach at the potential customers. While doing so they target their already existing customers who are the ambassadors of their company. To address the existing customers, they maintain the detailed customer data at all levels as customer maser data [9]. In this paper, we have built a prediction model to identify the customers who would most likely respond to the prospective offerings of the company basing on their past purchasing trends. Experiments have been conducted using the well known classifiers, viz., Naïve Bayes, KNN and SVM to classify a bank customer data. Subsequently, we have compared the effectiveness of these techniques and found out which one produces the maximum accuracy for the existing data set. © 2015 IEEE.

2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

A customer classification prediction model based on machine learning techniques

International Journal of Computer Science and Information Technology

Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System Using Neural Network

Scientific Data Mining and Knowledge Discovery

Data Mining and Discovery of Astronomical Knowledge

International Journal of Document Analysis and Recognition (IJDAR)

Word spotting for historical documents

Journal	Data powered by Typeset2017 International Conference on Computer Communication and Informatics (ICCCI)
Publisher	Data powered by TypesetIEEE
Open Access	No