Handwritten alphabets recognition using twelve directional feature extraction and self organizing maps

Recognizing pattern of handwriting has long been identified as a difficult problem needs to be solved by a computer. The main challenges are handwriting dynamicity and various forms or shapes of alphabet. Thus, computer requires several complex processes which are image processing, feature extraction and alphabets recognition. This research proposes an offline Handwritten Alphabets Recognition (HAR) automated system using Twelve Directional feature extraction and Self Organizing Maps (SOM) clustering algorithm to effectively recognize the type of alphabets. The proposed HAR system has three components: 1) preprocessing: which consists of grayscale image conversion, binarization and thinning, 2) feature extraction: that based on twelve directional feature input, and 3) clustering: using SOM algorithm. Experiments have been conducted on primary dataset and secondary dataset from benchmarked chars74k dataset. The results have shown that it produces encouraging recognition performance with 90% accuracy (for 150 secondary data) and 87.69% (for 150 primary data). This indicates that the proposed system can be an alternative solution to efficiently recognize the handwritten alphabets.