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

This paper deals with a new method for recognition of offline Handwrittennon-compound Devnagari Characters in two stages. It uses two well known andestablished pattern recognition techniques: one using neural networks and theother one using minimum edit distance. Each of these techniques is applied ondifferent sets of characters for recognition. In the first stage, two sets offeatures are computed and two classifiers are applied to get higher recognitionaccuracy. Two MLP's are used separately to recognize the characters. For one ofthe MLP's the characters are represented with their shadow features and for theother chain code histogram feature is used. The decision of both MLP's iscombined using weighted majority scheme. Top three results produced by combinedMLP's in the first stage are used to calculate the relative difference values.In the second stage, based on these relative differences character set isdivided into two. First set consists of the characters with distinct shapes andsecond set consists of confused characters, which appear very similar inshapes. Characters of distinct shapes of first set are classified using MLP.Confused characters in second set are classified using minimum edit distancemethod. Method of minimum edit distance makes use of corner detected in acharacter image using modified Harris corner detection technique. Experiment onthis method is carried out on a database of 7154 samples. The overallrecognition is found to be 90.74%.

Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance