A Deep study on EPSO-EKNN Algorithm as compared to DBN Algorithm

Offline character recognition has become a highly important study field for various pattern recognition applications in recent years. Several handwritten character recognition systems have been suggested, with the complexity of these systems varying depending on the recognizing units' writing styles. In reality, identifying letters. or numerals is far simpler than recognizing cursive sentences or lines of text. As a result, early handwriting recognition algorithms could only distinguish a few characters with limited vocabularies. Nowadays Arabic handwritten character recognition is very important as it is very difficult to identify. The cursive writing and variety of styles make this recognition more complex. This paper presents an automated model for ACR. This ACR is constructed from four phases: Preprocessing, Segmentation, Feature Extraction, and Classification. In this research article, We compare the advanced EPSO EKNN Algorithm with the earlier DBN Algorithm and also suggest a new method having higher Accuracy.