Handwritten digit recognition: investigation of normalization and feature extraction techniques
The performance evaluation of various techniques is important to select the correct options in developing character recognition systems. In our previous works, we have proposed aspect ratio adaptive normalization (ARAN) and have evaluated the performance of state-of-the-art feature extraction and classification techniques. For this time, we will propose some improved normalization functions and direction feature extraction strategies and will compare their performance with existing techniques. We compare ten normalization functions (seven based on dimensions and three based on moments) and eight feature vectors on three distinct data sources. The normalization functions and feature vectors are combined to produce eighty classification accuracies to each dataset. The comparison of normalization functions shows that moment-based functions outperform the dimension-based ones and the aspect ratio mapping is influential. The comparison of feature vectors shows that the improved feature extraction strategies outperform their baseline counterparts. The gradient feature from gray-scale image mostly yields the best performance and the improved NCFE (normalization-cooperated feature extraction) features also perform well. The combined effects of normalization, feature extraction, and classification have yielded very high accuracies on well-known datasets.
handwritten digit recognition normalization direction feature aspect ratio mapping gradient feature ncfe