A case study on mathematical expression recognition to GPU

The technology of mathematical expression identification and recognition extracts mathematical expressions in document images, and it has been studied for over a decade. Based on previous works, we develop an automatic recognition tool, named EqnEye, which leverages the OpenCV library to perform image processing and Tesseract tool to recognize mathematical symbols. We also apply correction methods before the recognition stage to improve the recognition accuracy. To improve the efficiency for processing images of high resolution, the parallel implementation of thresholding method on GPU is integrated into this work. Experimental results exhibit the success of our correction methods to enhance the accuracy and the slight improvement to the performance. In addition, porting the recognition tool to handy devices can produce more value-added applications.