Abstract: Because the effective strokes of offline handwritten signature images are generally sparse, and there are lots of invalid white backgrounds, using the commonly used feature description methods will cause a lot of re-dundancy in the obtained feature data, which will affect the recognition accuracy. In order to improve the recognition accuracy, we either need to rely on a large number of training data or extract multiple features for fusion, which will cause difficulty in the calculation and affect the efficiency of the experiment due to too much feature data and too large dimensions. Therefore, this paper proposes a multilingual off-line hand-written signature recognition method based on the Gist and IPCA algorithms, which uses gist features to focus on the overall layout and strokes of the image, and the batch processing ability of the IPCA algorithm to improve the recognition effect and operation efficiency. Three experimental datasets (Chinese, English, and Uyghur) and the SVM classifier are used in the recognition experiments. The results show that the recognition accuracy of the three data sets is 97.97%, 98.43%, and 97.19% respectively, and the recognition accuracy of the three mixed data sets is 97.7%. Comparative analysis shows that the proposal is obviously better than the previous related research.

Key words: multilingual, handwritten signature recognition, Gist feature, IPCA algorithm, SVM