Scene text rectification using glyph and character alignment properties
Taeho Kil, Hyung Il Koo, Nam Ik Cho · 2018
Scene text images usually suffer from perspective distortions, and hence their rectification has been an essential pre-processing step for many applications. Existing methods for scene text rectification mainly exploited the glyph property, which means that the characters in many languages have horizontal/vertical strokes and also have some symmetries in their shapes. In this paper, we propose to use an additional property that the characters need to be well aligned when rectified. For this, character alignment, as well as glyph properties, are encoded in the proposed cost function, and its minimization generates the transformation parameters. For encoding the alignment constraints, we perform the character segmentation using a projection profile method before optimizing the cost function. Since better segmentation needs better rectification and vice versa, the overall algorithm is designed to perform character segmentation and rectification iteratively. We evaluate our method on real and synthetic scene text images, and the experimental results show that our method achieves higher optical character recognition (OCR) rate than the previous approaches and also yields visually pleasing results.