Character extraction from documents using wavelet maxima

摘要

The extraction of character images is an important front-end processing task in optical character recognition (OCR) and other applications. This process is extremely important because OCR applications usually extract salient features and process them. The existence of noise not only destroys features of characters, but also introduces unwanted features. We propose a new algorithm which removes unwanted background noise from a textual image. Our algorithm is based on the observation that the magnitude of the intensity variation of character boundaries differs from that of noise at various scales of their wavelet transform. Therefore, most of the edges corresponding to the character boundaries at each scale can be extracted using a thresholding method. The internal region of a character is determined by a voting procedure, which uses the arguments of the remaining edges. The interior of the recovered characters is solid, containing no holes. The recovered characters tend to become fattened because of the smoothness applied in the calculation of the wavelet transform. To obtain a quality restoration of the character image, the precise locations of characters in the original image are then estimated using a Bayesian criterion. We also present some experimental results that suggest the effectiveness of our method.