Evaluating synthetic pre-Training for handwriting processing tasks.

• We consider large-scale supervised pre-training on a carefully designed synthetic dataset of word images. • We obtain robust writer's style representations, independent of the semantic content of the image. • We leverage the obtained representations for handwriting analysis tasks on real images from benchmark datasets. • Experiments demonstrate the suitability of our approach and its competitiveness compared to task-specific state-of-the-art. In this work, we explore massive pre-training on synthetic word images for enhancing the performance on four benchmark downstream handwriting analysis tasks. To this end, we build a large synthetic dataset of word images rendered in several handwriting fonts, which offers a complete supervision signal. We use it to train a simple convolutional neural network (ConvNet) with a fully supervised objective. The vector representations of the images obtained from the pre-trained ConvNet can then be considered as encodings of the handwriting style. We exploit such representations for Writer Retrieval, Writer Identification, Writer Verification, and Writer Classification and demonstrate that our pre-training strategy allows extracting rich representations of the writers' style that enable the aforementioned tasks with competitive results with respect to task-specific State-of-the-Art approaches. [ABSTRACT FROM AUTHOR]