Rectification of Optical Characters as Transform Invariant Low-rank Textures


Character rectification is very important for character recognition. Front view standard character images are much easier to recognize since most character recognition algorithms were trained with such data. However, the existing text rectification methods only work for a paragraph or a page. We discover that the modified TILT algorithm can be applied to rectify many single Chinese, English, and digit characters robustly. By changing the character image into a low-rank texture image via binarization and graylevel inversion, the modified TILT method applies a rank minimization technique to recover the deformation and the proposed algorithm can work for almost all characters. To further enhance the robustness of the proposed algorithm, the modified TILT algorithm is extended for short phrases that consist of multiple characters. Extensive experiments testify to the effectiveness of the proposed method in rectifying texts with significant affine or perspective deformation in real images, such as street signs taken by mobile phones.

International Conference on Document Analysis and Recognition