Text and Non-text Separation in Scanned Color-Official Documents

摘要

Official documents consist of text and non-textual elements such as logo, stamp, and signature. Separation of these elements from a scanned document plays a significant role in document image retrieval, recognition, and verification. This paper presents a novel scheme to separate text and non-text elements of official documents using part-based features. In this work, we exploit the fact that intensity distributions of text and non-text elements in HSV color space are of distinctive nature. A new approach to compute part-based features using S and V channels is proposed. The classification of text and non-text components is performed based on majority voting scheme and K-approximate nearest neighbors. The knowledge base acquired during training is indexed using kD-tree indexing scheme. Subsequently, the method is extended for detection of logo, stamp, and signature. Experimental results show the effectiveness of the proposed approach.