The Chinese language and literature layout has important application value. How to quickly and efficiently deal with the huge amount, diverse forms, and complex content of Chinese language and literature is a problem that people pay more attention to. In addition, a large number of digital resources exist in the form of images instead of text encoding. How to efficiently manage and use this document information, especially the fast retrieval of document content, is an important research direction. This paper mainly discusses the processing of Chinese language and literature layout combined with text image preprocessing algorithm and researches into the skew correction of document images. There are many types of documents, and many tilt correction methods are based on prior knowledge. If such a sample quality situation occurs, the image needs to be preprocessed. Using denoising to remove useless noise information and using correction technology to leave and strengthen the classified effective information, we can carry out the next step of retrieval and recognition. The preprocessing avoids interference and destruction of the feature description algorithm by other factors as much as possible and guarantees the effect of recognition and retrieval to a certain extent. This paper proposes a method of document image tilt correction based on the content of the document. According to different document contents, we select the corresponding strategy to estimate the document tilt. This paper uses two-dimensional wavelet transform, runs length smoothing and thinning preprocessing to extract the lines and text lines in the document image, and uses the least square method of linear parameters to estimate the inclination angle of the document. This method has the characteristics of high accuracy of tilt estimation and strong adaptability. The accuracy of feature line matching in geometric structure classification can reach 97%. This research helps to promote the continuous development of Chinese language and literature.