Evaluating Synthetic Pre-Training for Handwriting Processing Tasks
This work addresses handwriting processing for document analysis and forensics, but it is incremental as it applies an existing pre-training strategy to a new synthetic dataset.
The authors tackled the problem of improving handwriting analysis tasks by pre-training a convolutional neural network on a large synthetic dataset of word images, achieving competitive results on writer retrieval, identification, verification, and classification compared to state-of-the-art methods.
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.