Diagonal Based Feature Extraction and Backpropagation Neural Network in Handwritten Batak Toba Characters Recognition

Abstract

Handwritten character recognition is considered a complex problem since one’s handwritten character has its characteristics. Data used for this research was a photo of handwritten or scanned handwritten. In this research, Backpropagation Neural Network (BPNN) was used to recognize handwritten Batak Toba character, wherein preprocessing stage feature extraction was done using Diagonal Based Feature Extraction (DBFE) to obtain feature value. Furthermore, the feature value will be used as an input to BPNN. The total number of data used was190 data, where 114 data was used for the training process and another 76 data was used for testing. From the testing process carried out, the accuracy obtained was 87,19 %.

Image preprocessing analysis in handwritten Javanese character recognition

“…Indonesia has more than one traditional script because Indonesia consists of various regional tribes, each tribe has cultural diversity, and some even have tribe letters. For example, the Javanese tribe has a Javanese script, the Sunda tribe has aksara Sunda [1], Lampung has aksara Kaganga [2], Lombok island has aksara Sasak [3], Makasar has aksara Lontara [4], and Batak tribe has aksara Batak [5]. Javanese script consists of 20 letters that come from the legend of Ajisaka.…”

Neural Network Classification in Javanese Handwriting Recognition using Projection Profile Histogram and Local Binary Pattern Histogram

“…Next we will focus on feature extraction and classification methods Several classification methods used in previous studies are k-NN [3], [4], neural networks [4]- [7], CNN [7]- [9], and SVM [10]. And several feature extraction in handwritten Javanese letter are zoning [11], ICZ-ZCZ [12]- [14], LBP [10], [15], [16], and HOG [17], [18]. Budhi [9].…”