The irregular distribution of text is a significant challenge for current scene text recognizers. Rectification methods based on Thin-Plate Spline (TPS) are adopted by various recognizers due to their plug-and-play nature. However, since the TPS transformation parameters are determined based on text boundaries regressed, it struggles to focus on the position of text areas relative to the image and fails to suppress background interference. Although the rectified images retain the geometric structure of the text, this leads to unnatural character deformation and characters exceeding the image boundaries. To address these issues, this paper proposes a scene text recognition algorithm based on corner points and attention mechanisms. Firstly, a novel irregular text rectification algorithm named CornerTPS is introduced, which integrates corner features with image features, allowing the TPS transformation to focus on character position information and improving the rectification effect for irregular text. Then, CornerTPS is improved to a feature-level rectification, and the rectified corner feature map is integrated into the encoder, further guiding the encoder to focus on text area features and reducing the interference of non-text area corners. Experiments on public datasets demonstrate that the algorithm proposed in this paper has a significant advantage in irregular text recognition.