An offline text-independent writer identification system with SAE feature extraction

摘要

This paper presents an offline text-independent writer identification system in a multi-language environment using sparse auto-encoder (SAE) based codebook (SAEC) and non-text-segmentation feature extraction methods. The proposed codebook is designed by SAE structure and clustering. The superior of the designed frame work is that features can be effectively extracted and without the pre-step of text segmentation. The novel codebook can deal with mixed-languages at the same time. Hand-written texts in Chinese and English are considered in this study. We use both HIT Chinese database and IAM offline English database. The classification rate achieves the challenging accuracy of 95.56% concerning top 1 and 99.17% concerning top 10 on the two mixed databases. Another interesting aspect of our study is the evaluation of the factors such as patch sizes, patch numbers and the amount of text that influence the identification results.