The rectification and recognition of document images with perspective and geometric distortions

Abstract

As sensor resolution increases in recent years, high-speed non-contact text capture through a digital camera is opening up a new channel for document capturing and processing. This thesis presents a new technique using fuzzy set and morphological operations, which is capable of rectifying and recognizing document images with per-spective and geometric distortions. The proposed technique carries out the document distortion correction based on identified vertical character stroke boundary and fitted top line and base line of text lines using fuzzy set and morphological operations. The recognition algorithm classifies captured document text through the exploitation of perspective invariants such as Euler number and intersection numbers. Experimental results show the proposed document rectification algorithm is accurate, fast, and much easier to implement than the existing approaches reported in the literature. The recognition experiments over 150 distorted document images show the recognition rate with the proposed technique reaches over 93%.