Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

Abstract

In today's digital era, the text may be in form of images. This research aims to deal with the problem by recognizing such text and utilizing the support vector machine (SVM). A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language. A method is developed for identifying Hindi language characters that use morphology, edge detection, histograms of oriented gradients (HOG), and SVM classes for summary creation. SVM rank employs the summary to extract essential phrases based on paragraph position, phrase position, numerical data, inverted comma, sentence length, and keywords features. The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features. The second goal is to maintain or improve the classification system's performance. The experiment included news articles from various genres, such as Bollywood, politics, and sports. The proposed method's accuracy for Hindi character recognition is 96.97%, which is good compared with baseline approaches, and system-generated summaries are compared to human summaries. The evaluated results show a precision of 72% at a compression ratio of 50% and a precision of 60% at a compression ratio of 25%, in comparison to state-of-the-art methods, this is a decent result.

Semantic Orientation of Crosslingual Sentiments: Employment of Lexicon and Dictionaries

“…One another solution to multilingualism is facial recognition and it has been reported that facial recognition based sentiment analysis has gained noticeable attention due to the advanced technology in commercial and industrial applications such as smart Master card for online transaction, health related devices, character recognition, IoT, Post Pandemic World, pain detection, criminal identification and security surveillance [49], [50], [51], [52], [53], [54], [55]. HF-CSA makes it possible to sort and utilize unstructured text of resource poor languages in order to assess customer support issues and to support consumers' satisfaction, reputation management, brand monitoring, decision support systems and market analysis.…”

Quantum Fuzzy Support Vector Machine for Binary Classification

“…There are currently several types of SVM, such as twin SVM [1], Gaussian SVM [2], multi-kernel SVM [3] and so on. Furthermore, SVM has been widely applied in a variety of fields, for example, handwritten hindi character recognition [4], face recognition [5], network intrusion detection [6] and breast cancer diagnosis [7], and so on. Although SVM is a common algorithm for classification problems that treats all samples equally and ignores the effect of outliers and noise on the construction of optimal hyperplanes, it fails to perform well when classifying new sets of data with fuzzy information.…”