English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Received: 20 August 2019 Accepted: 12 October 2019 This paper presents influence of Histogram of Oriented Gradients (HOG) features on three different popular classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and Neural Network (NN). Devanagari script is one among the foremost standard scripts in India used in many languages. The main objective of our study is to recognize Handwritten Devanagari Characters using HOG features and also examine the efficiency of HOG features on it using multiple cells of HOG descriptor. Here, presented Handwritten Devanagari Character Recognition System (HDCRS) is used to recognize isolated characters by making use of SVM, K-NN and NN well known classifiers. HOG is very effective and dense technique used to extract features from every positions of an image by dividing images into cells. HOG tries to capture shape of an image by using gradients information. The features extracted are classified using above mentioned classifiers. Through this study, we observed that SVM+HOG combination achieved highest accuracy: a recognition rate of 87.38%. The findings of this research may help in digitization of Handwritten Devanagari Documents.

Histogram of Oriented Gradients Based Off-Line Handwritten Devanagari Characters Recognition Using SVM, K-NN and NN Classifiers