Affiliations: [a] Faculty of Modern Languages and Communication, Universiti Putra Malaysia, Selangor, Malaysia
| [b] School of Computer Science and Technology, Inner Mongolia University for Nationalities, Tongliao, China
Correspondence:
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Corresponding author. Ao Qi, Faculty of Modern Languages and Communication, Universiti Putra Malaysia, Selangor, Malaysia. E-mail: [email protected].
Abstract: At present, the recognition method based on character segmentation is not effective in recognizing English text, and the traditional methods are based on the structural features and statistical characteristics of strokes. In order to improve the recognition effect of in English text, from the perspective of machine learning, this study introduces multi-features to improve the lack of information caused by the small Chinese data set. Moreover, this study disassembles the character recognition problem into a text matching problem of question and answer, and the textual entailment problem of answer and standard answer and continues training on the data set of short text score. The final result has a certain improvement, which proves the usability of the mechanism designed in this paper. In order to study the performance of the model proposed in this paper, the model proposed in this paper and the neural network recognition model are compared in terms of recognition accuracy and recognition speed. The research results show that the algorithm proposed in this paper has a certain effect.
