Parameter-Free Geometric Document Layout Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Document Spectrum for Page Layout Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Page segmentation and classification using fast feature extraction and connectivity analysis
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Page segmentation and classification utilising a bottom-up approach
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
A System for Automatic Chinese Business Card Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
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In this paper, we present a method of region analysis for business card images acquired in a PDA (personal digital assistant) using DCT and information pixel (IP) density. The proposed method consists of three parts: region segmentation, information region (IR) classification, and character region (CR) classification. In the region segmentation, an input business card image is partitioned into 8 × 8 blocks and the blocks are classified into information blocks (IBs) and background blocks (BBs) by a normalized DCT energy. The input image is then segmented into IRs and background regions (BRs) by region labeling on the classified blocks. In the IR classification, each IR is classified into CR or picture region (PR) by using a ratio of DCT energy of edges in horizontal and vertical directions to DCT energy of low frequency components and a density of IPs. In the CR classification, each CR is classified into large CR (LCR) or small CR (SCR) by using the density of IPs and an averaged run-length of IPs. Experimental results show that the proposed region analysis yields good performance for test images of several types of business cards acquired in a PDA under various surrounding conditions. In addition, error rates of the proposed method are shown to be 2.2–10.1% lower in region segmentation and 7.7% lower in IR classification than those of the conventional methods.