Performance characterization and acceleration of Optical Character Recognition on handheld platforms

Optical Character Recognition (OCR) converts images of handwritten or printed text captured by camera or scanner into editable text. OCR has seen limited adoption in mobile platforms due to the performance constraints of these systems. Intel^® Atom™ processors have enabled general purpose applications to be executed on handheld devices. In this paper, we analyze a reference implementation of the OCR workload on a low power general purpose processor and identify the primary hotspot functions that incur a large fraction of the overall response time. We also present a detailed architectural characterization of the hotspot functions in terms of CPI, MPI, etc. We then implement and analyze several software/algorithmic optimizations such as i) Multi-threading, ii) image sampling for a hotspot function and Hi) miscellaneous code optimization. Our results show that up to 2X performance improvement in execution time of the application and almost 9X improvement for a hotspot can be achieved by using various software optimizations. We designed and implemented a hardware accelerator for one of the hotspots to further reduce the execution time and power. Overall, we believe our analysis provides a detailed understanding of the processing overheads for OCR running on a new class of low power compute platforms.