Application of convolutional neural networks for pattern recognition circuits of railway automatics. Specifics of this application

The paper proposes the improved skeleton method of handwritten characters recognition, which is based on the filtering procedure and the principle of alternating shading schemes of skeletonized area on the 4- and 8-timeslinked raster. The procedure of high-frequency filtration based on discrete real cosine transformation or discrete complex Fourier transform with automatic selection of filtration parameters makes it possible to significantly improve the image quality of handwritten symbols, in particular, to eliminate in many cases thin bridges between the areas of symbol element representation. The principle of alternating the painting schemes along the 4- and 8-timeslinked raster makes it possible to get the wave front of the skeletonized area close to a circle. In this case, the broken lines representing the branches of the skeleton graphs retain the shapes of the symbols. Numerical experiments on the construction of skeleton sets and skeleton graphs for recognizable handwritten symbols located in the cells of the tables of logistic transport problems have been performed. Software implementation of the method is proposed.

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The Using of Electronic Document Management Tools of Technical Documentation for the Assessment of the Life of the Train Traffic Control Devices

Sedykh

Zuyev

Vasilenko

et al. 2019

2019 IEEE East-West Design &Amp; Test Symposium (EWDTS)

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Application of convolutional neural networks for pattern recognition circuits of railway automatics. Specifics of this application

Cited by 4 publications

References 2 publications

Automatic Recognition of Fuzzy Characters in the Transport Task Tables of Scanned Handwritten Student Papers

Automatic Recognition of Fuzzy Characters in the Transport Task Tables of Scanned Handwritten Student Papers

Offline character recognition in transport logistic tables

The Using of Electronic Document Management Tools of Technical Documentation for the Assessment of the Life of the Train Traffic Control Devices

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