Automatic correction system of elementary arithmetic images based on deep learning

2020 ◽  
Vol 1651 ◽  
pp. 012152
Author(s):  
Mingtao Liu ◽  
Xinyu Xia
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chen Hongli

In order to solve the problems of low correction accuracy and long correction time in the traditional English grammar error correction system, an English grammar error correction system based on deep learning is designed in this paper. This method analyzes the business requirements and functions of the English grammar error correction system and then designs the overall architecture of the system according to the analysis results, including English grammar error correction module, service access module, and feedback filtering module. The multilayer feedforward neural network is used to construct the language model to judge whether the language sequence is a normal sentence, so as to complete the correction of English grammatical errors. The experimental results show that the designed system has high accuracy and fast speed in correcting English grammatical errors.


Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 19 ◽  
Author(s):  
Maha Alamri ◽  
William Teahan

This paper proposes an automatic correction system that detects and corrects dyslexic errors in Arabic text. The system uses a language model based on the Prediction by Partial Matching (PPM) text compression scheme that generates possible alternatives for each misspelled word. Furthermore, the generated candidate list is based on edit operations (insertion, deletion, substitution and transposition), and the correct alternative for each misspelled word is chosen on the basis of the compression codelength of the trigram. The system is compared with widely-used Arabic word processing software and the Farasa tool. The system provided good results compared with the other tools, with a recall of 43%, precision 89%, F1 58% and accuracy 81%.


1978 ◽  
Vol 32 (1) ◽  
pp. 46-53 ◽  
Author(s):  
Gilbert B. Chapman ◽  
William A. Gordon

This procedure provides automatic correction for drifts in the radiometric sensitivity of each detector channel in a direct-reading emission spectrometer. Such drifts are customarily controlled by the regular analyses of standards, which provide corrections for changes in the excitational, optical, and electronic components of the instrument. This standardization procedure, however, corrects for the optical and electronic drifts, thus minimizing the time, effort, and cost of regularly processing standards. This method of radiometric drift correction uses a 1000-W tungsten-halogen reference lamp to illuminate each detector through the same optical path as that traversed during sample analysis. The responses of the detector channels to this reference light are regularly compared with channel responses to the same light intensity at the time of analytical calibration in order to determine and correct for drift. The coefficients of variation of these drift corrections average less than 1%. Except for placing the lamp in position, the procedure is fully automated and compensates for changes in spectral intensity due to variations in lamp current. A discussion of the implementation of this drift-correction system is included.


2011 ◽  
Vol 464 ◽  
pp. 155-158 ◽  
Author(s):  
Tian Xing Li ◽  
Xiao Zhong Deng ◽  
Zhen Shan Gao ◽  
Ju Bo Li

The system of automatic correction and deviation measurement of hypoid gears is the basic platform for the digital closed-loop manufacturing technology. Based on the gear measuring center and the numerical controlled gear milling machine, a measurement and correction system is developed by the application of Visual C++ and Fortran. The architecture and the implement of the main modules are elaborated. Experiments and applications indicate that the tooth surface deviation can be effectively reduced by the system of automatic correction and measurement, and the stability of tooth surface precision and manufacturing quality is improved. It would provide the foundation for the digitalization of manufacture and quality control of hypoid gears.


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