Improved Arabic Word Classification using Spatial Pyramid Matching Method

In recent years, rapidly developed hand written word recognition techniques have attracted researcher’s attention to study Arabic word classification. Arabic language has cursive style of writing so it needs special framework for classification. In this paper, a precise framework for Arabic word classification is presented, which uses sparse coding with spatial pyramid matching (SPM) algorithm and linear support vector machine classifier. SPM maps each feature set to a multi-resolution histogram that preserves the individual feature at the finest level. The histogram pyramids are then compared by using a weighted histogram intersection algorithm. Our proposed framework is evaluated with four publically available datasets; IFN/ENIT, PATS-A01, IFHCDB and ISI Bangla numeral. Experimental results show that the proposed framework outperforms those state of art methods used for Arabic words classification. KeywordsArabic Character Recognition; linear support vector machine (LSVM); Spatial Pyramid Matching (SPM); Scale invariant feature transform (SIFT).