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Analysis Framework of Comparative Literature Wisdom Model based on Cyclic Text Recognition Algorithm

In the recent and latest study, the existing models regarding text recognition with the combination of deep neural network is general focused on the end connected end models, that is, it does not require explicit text cutting, but converts direct text recognition into a sequence learning problem. Hence, the analysis framework of comparative literature wisdom model based on the cyclic text recognition algorithm is studied in the paper. Recurrent neural network has the specia functionality enhancement feature of meetwing with the sequence scenarios, but RNN will have gradient disappearance as the time interval increases. Therefore, the designed text model is then considered as the solution. Through the systematic simulation, the accuracy performance is validated.