WRITER IDENTIFICATION: THE EFFECT OF IMAGE RESIZING ON CNN PERFORMANCE

Abstract. Introducing Deep Learning has been successful in improving the performance of automated writer identification systems. However, using very large patch sizes as input to CNN consumes a lot of machine resources and requires a lot of training time. To overcome this problem, many researchers use resized images.

In this paper, we will try to make a comparative study between several patches sizes which were then resized to a normalized size of 32 × 32. Our aim is to elaborate the best recommendations for choosing the image resizing in order to increase the CNN performance. Thus, we will carry our tests on three databases. The first is CVL, a Latin dataset with 310 writers, the second is CERUG-CH a Chinese dataset with 105 writers and the last is KHATT that contains the Arabic writings of 1000 writers. To see if the type of CNN model impacts the results conducted on resized images, we deploy two models: ResNet-18 and MobileNet. The main finding is that the best results correspond to the resizing values of the images which makes it possible to have the average line height of the writings closer to the height of the CNN patches.