SPIE 2020 Papers
作者：XD / 发表： 2020年5月9日 16:53 / 更新： 2020年5月9日 16:53 / 科研学习 / 阅读量：4615
Dong Xie and Colleen P. Bailey "Novel receipt recognition with deep learning algorithms", Proc. SPIE 11400, Pattern Recognition and Tracking XXXI, 114000B (22 April 2020); https://doi.org/10.1117/12.2558206
We propose a new recognition method to extract effective information from receipts by integrating deep learning algorithms from computer vision and natural language processing. Our method consists of three parts. The first part provides effective areas for receipt detection. By removing noise and extracting the gradient of the receipt image, we determine the threshold to crop and reshape the useful receipt area. Detecting text from a receipt image is the second part, we modify and deploy the text detection algorithm connectionist text proposal network (CTPN) to locate the text region in the receipt. In the third part, we import the connectionist temporal classification with maximum entropy regularization as the loss function for updating the convolutional recurrent neural networks (CRNN) to recognize the text detection area, which converts the receipt from an image into the text. Based on our method, the effective information of a receipt can be integrated and utilized. We train and test our system using the data set published by scanned receipts optical character recognition and information extraction (SROIE). The results illustrate that our recognition system is able to identify receipt information quickly and accurately.
Arthur C. Depoian, Lorenzo E Jaques, Dong Xie, Colleen P. Bailey, and Parthasarathy Guturu "Computer vision learning techniques for sports video analytics: removing overlays", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 113950M (24 April 2020); https://doi.org/10.1117/12.2560888
Big data has been driving professional sports over the last decade. In our data-driven world, it becomes important to find additional methods for the analysis of both games and athletes. There is an abundance of videos taken in professional and amateur sports. Player datasets can be created utilizing computer vision techniques. We propose a novel approach by creating an autonomous masking algorithm that can receive live or previously recorded video footage of sporting events. This procedure can identify graphical overlays to optimize further processing by tracking and text recognition algorithms for real-time analysis.