A Novel Approach for Arabic Character Recognition Using Hybrid SIFT-SVM

For more than three decades, recognizing handwriting has been a desirable goal for people looking to enter data into computer systems. The emergence of handwriting recognition technology is keenly anticipated across various fields. For several years, OCR systems have allowed computers to view characters as images and retrieve information about their distinct characteristics. Indeed, according to Arabic writing, which has benefited from a few researches. The main stage of identifying individual characters involves the use of machines to analyze hand-written documents. The identification of isolated Arabic characters is the primary focus of this research. The recognition of Arabic characters holds significant importance in the domain of computer vision, as it is crucial to accurately identify and categorize manuscript Arabic letters and characters. The objective of this paper is to propose a fresh methodology, which involves the utilization of Scale Invariant Feature Transform (SIFT) features of Arabic characters, Moreover, the Bag of Visual Words (BoVW) approach is used to classify the characteristics. Then those characteristics are grouped using K-means clustering in order to create a dictionary. The following stage involves using the Support Vector Machine (SVM) technique to categorize the word images in the codebook that was produced from these visual representations.