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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, convex hull based features are used for recognition ofisolated Roman numerals using a Multi Layer Perceptron (MLP) based classifier.Experiments of convex hull based features for handwritten character recognitionare few in numbers. Convex hull of a pattern and the centroid of the convexhull both are affine invariant attributes. In this work, 25 features areextracted based on different bays attributes of the convex hull of the digitpatterns. Then these patterns are divided into four sub-images with respect tothe centroid of the convex hull boundary. From each such sub-image 25 baysfeatures are also calculated. In all 125 convex hull based features areextracted for each numeric digit patterns under the current experiment. Theperformance of the designed feature set is tested on the standard MNIST dataset, consisting of 60000 training and 10000 test images of handwritten Romanusing an MLP based classifier a maximum success rate of 97.44% is achieved onthe test data.

Design of a novel convex hull based feature set for recognition of isolated handwritten Roman numerals