An enhanced LBP-based technique with various size of sliding window approach for handwritten Arabic digit recognition

• Published in: Multimedia tools and applications Vol. 80; no. 16; pp. 24399 - 24418
• Main Authors: Al-wajih, Ebrahim, Ghazali, Rozaida
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2021; Springer Nature B.V
• Subjects: Accuracy; Classifiers; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Feature extraction; Handwriting recognition; Multimedia Information Systems; Sliding; Special Purpose and Application-Based Systems; Support vector machines; Local binary pattern; Feature extraction; Pattern recognition; Sliding window; Arabic digit recognition
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-10762-x

Many variations of local binary pattern (LBP) were proposed to enhance its performance, including uniform local binary pattern (ULBP), center-symmetric local binary patterns (CS-LBP), center symmetric local ternary patterns (CS-LTP), center symmetric local multilevel pattern (CS-LMP), etc. In this paper, the accuracies of LBP technique and its variations are enhanced using four different sizes of a sliding window approach. This approach is used for investigating whether the features extracted by LBP are significant enough or its versions are needed as well. Five LBP-based techniques have been used including LBP, CS-LBP, CS-LTP, CS-LMP, and U2LBP. They have been applied to an Arabic digit image dataset called MAHDBase. Support vector machine (SVM) and random forests are utilized as classifiers. The experimental results show that the obtained accuracies have been improved by 19.56%, 21.43%, 5.63%, 6.51% and 5.62% for CS-LBP, CS-LMP, U2LBP, CS-LTP, and LBP, respectively, when the sliding window approach has been applied and SVM with linear kernel has been used as a classifier. Moreover, the results show that there is no need to use LBP variations to enhance the accuracy if the sliding window is applied because the highest accuracy has been acquired using LBP. At the end, the accuracy of proposed systems has been compared against other state-of-the-art LBP-based techniques showing the significance of the proposed systems.