Automated Recognition of Numeric Display Based on Deep Learning

This paper proposes approach to the automatic recognition of digits from the displays of medical devices such as blood pressure meters, glucometers, analyzers of harmful substances content. A fully connected, convolutional model of deep learning with the use of tensor flow and keras deep learning libraries has been developed. A database was created for training and validation of the proposed network, which contains 4500 images. The evaluation of the proposed recognition system for a large number of images has been carried out. The optimization of the model for practical use with CPU and GPU is carried out and the average performance time is shown.

[1]  Shi,et al.  A Fast Algorithm for Finding Crosswalks using Figure-Ground Segmentation , 2006 .

[2]  Bohdan Rusyn,et al.  Segmentation of atmospheric clouds images obtained by remote sensing , 2018, 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET).

[4]  Anil K. Jain,et al.  Automatic text location in images and video frames , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[5]  Minho Jo,et al.  Probabilistic Recovery of Incomplete Sensed Data in IoT , 2018, IEEE Internet of Things Journal.

[6]  Bogdan P. Rusyn,et al.  Modified Architecture of Lossless Image Compression Based on FPGA for On-Board Devices with Linear CCD , 2019, Journal of Automation and Information Sciences.

[7]  He Huang,et al.  Car plate character recognition using a convolutional neural network with shared hidden layers , 2015, 2015 Chinese Automation Congress (CAC).

[8]  Koen E. A. van de Sande,et al.  Automatic Auroral Detection in Color All-Sky Camera Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  B. P. Rusyn,et al.  Upper-bound estimates for classifiers based on a dissimilarity function , 2012 .

[10]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[11]  Noman Islam,et al.  A Survey on Optical Character Recognition System , 2017, ArXiv.

[12]  Changming Sun,et al.  An End-to-End TextSpotter with Explicit Alignment and Attention , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.