GPGPU-based High Throughput Image Pre-processing Towards Large-Scale Optical Character Recognition

Studies have shown that pre-processing digital images through scaling, rotation and blurring type of operations allow optical character recognition (OCR) to focus on the key features in the image and result in improving recognition accuracy. We leverage the open-source Tesseract OCR and show that its accuracy can be improved through a pre-processing flow that includes thresholding, rotation, rescaling, erosion, dilation, and noise removal steps based on a dataset that is formed of 560 phone screen images. However, the serial CPU-based implementation of this flow introduces a latency of 48.32 ms per image on average. Even though time scale is low in the context of a single image, this latency poses as a barrier when processing millions of images with OCR. To address this, we parallelize the entire pre-processing flow on the Nvidia P100 GPU, implement a streaming based execution, and reduce the latency to 0.846 ms. This streaming-enabled implementation enables setting up a GPU based OCR engine to process large scale workloads.