GPU parallel implementation of the new hybrid binarization based on Kmeans method (HBK)

International audienceThe Optical Character Recognition (OCR) is a process that converts characters within images into text documents. In paperless applications, OCR systems have to ensure a better accuracy as well as a high speed. One of the most important steps in OCR is binarization. In this context,we proposed recently the hybrid binarization-based Kmeans method (HBK) (Soua et al. in International Symposium on Communications, Control, and Signal Processing, 2014). HBK offers a satisfying recognition rate while scoring 91 % accuracy. In the other hand, running on an Intel Core i3 CPU processor, the HBK requires at least 1.9 s to process one A4 300 dpi document. However, binarization step should not exceed 460 ms in our real-time OCR system. For this, we propose in this paper a parallel implementation of the HBK method on the NVIDIA GTX 660 graphic processing unit (GPU). Our implementation combines fine-grained and coarse-grained parallelism strategies for the best GPU use. In addition, the costly CPU–GPU communication overhead is avoided and an efficient memory management is ensured. The effectiveness of our implementation is validated through extensive experiments, which demonstrate that the proposed HBK parallelization accelerates the studied process. Indeed, we ensure the binarization of one document in just 425 ms. Consequently, the implemented design is able to meet the targeted real-time OCR system in paperless application