Intelligent Work Order Recognition System Based on End-to-End Deep Neural Network
| Autor: | Guo Yang, Shifeng Fan, Fu Xin, Guannan Wang, Sining Wang, Lufeng Yuan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Data processing
Artificial neural network Computer science business.industry 02 engineering and technology Machine learning computer.software_genre Grid Numeral system Work order End-to-end principle 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence Chinese characters business computer |
| Zdroj: | 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). |
| DOI: | 10.1109/icivc47709.2019.8980851 |
| Popis: | As the largest public service provider in the world, State Grid Corporation of China (SGCC) generates huge amounts of data every day, much of which are paper work order in offline businesses. Paper work orders greatly affect office efficiency and reduce the data processing capacity. To solve this problem, technicians of SGCC implement an intelligent work order recognition system. This system based on deep neural network such as CNN, CRNN, uses graphical morphology analysis, segmentation, recognizes handwritten numerals, handwritten Chinese characters and printed characters automatically, imports various paper work orders rapidly. In practice, the accuracy of handwritten numeral recognition is 96%, the accuracy of handwritten Chinese character recognition is 94%, the accuracy of printed character recognition is 99%. This system greatly improves the work order import efficiency of grass-roots staff. |
| Databáze: | OpenAIRE |
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