The basic idea of converting text into pictures for duplicate checking is to convert the text information in the paper into pictures, and then judge whether there is too high similarity in the paper by comparing the similarity of pictures. This method has certain advantages. First, after the text is converted into pictures, the original information will be hidden, which reduces the possibility of being copied by others. Secondly, by comparing the similarity of pictures, we can rule out some conventional paper modification behaviors, such as repeated citations between papers of the same author. Finally, because words are transformed into pictures, the characteristics of pictures are more complex and diverse, and it is difficult for plagiarists to crack them easily.

However, there are still some disputes about whether the pictures after text conversion can pass the duplicate check of the paper. On the one hand, converting words into pictures will lose some information, which may lead to some similar but not exactly the same papers being judged as completely similar, which may misjudge some papers. On the other hand, because the characteristics of the picture are too complex, it may lead to the slow running speed of the duplicate checking system, thus affecting the user experience.

In order to solve these problems, it is necessary to further improve the technology of converting words into pictures. One possible solution is to use more advanced image processing algorithms to improve the accuracy of image similarity comparison. In addition, through the implementation of the optimization algorithm, the operation efficiency of the duplicate checking system can be improved, thus reducing the waiting time of users.