JAR tool: using document analysis for improving the throughput of high performance printing environments

Abstract

Digital printers have consistently improved their speed in the past years. Meanwhile, the need for document personalization and customization has increased. As a consequence of these two facts, the traditional rasterization process has become a highly demanding computational step in the printing workflow. Moreover, Print Service Providers are now using multiple RIP engines to speed up the whole document rasterization process, and depending on the input document characteristics the rasterization process may not achieve the print-engine speed creating a unwanted bottleneck. In this scenario, we developed a tool called Job Adaptive Router (JAR) aiming at improving the throughput of the rasterization process through a clever load balance among RIP engines which is based on information obtained by the analysis of input documents content. Furthermore, along with this tool we propose some strategies that consider relevant characteristics of documents, such as transparency and reusability of images, to split the job in a more intelligent way. The obtained results confirm that the use of the proposed tool improved the rasterization process performance.