In recent years there have been multiple successful attempts tackling document processing problems separately by designing task specific hand-tuned strategies. We argue that the diversity of historical document processing tasks prohibits to solve them one at a time and shows a need for designing generic approaches in order to handle the variability of historical series. In this paper, we address multiple tasks simultaneously such as page extraction, baseline extraction, layout analysis or multiple typologies of illustrations and photograph extraction. We propose an open-source implementation of a CNN-based pixel-wise predictor coupled with task dependent post-processing blocks. We show that a single CNN-architecture can be used across tasks with competitive results. Moreover most of the task-specific post-precessing steps can be decomposed in a small number of simple and standard reusable operations, adding to the flexibility of our approach.

The slowing down of Moore's law and the emergence of new technologies puts an increasing pressure on the field of EDA. There is a constant need to improve optimization algorithms. However, finding and implementing such algorithms is a difficult task, especially with the novel logic primitives and potentially unconventional requirements of emerging technologies. In this paper, we cast logic optimization as a deterministic Markov decision process (MDP). We then take advantage of recent advances in deep reinforcement learning to build a system that learns how to navigate this process. Our design has a number of desirable properties. It is autonomous because it learns automatically and does not require human intervention. It generalizes to large functions after training on small examples. Additionally, it intrinsically supports both single- and multi-output functions, without the need to handle special cases. Finally, it is generic because the same algorithm can be used to achieve different optimization objectives, e.g., size and depth.