Search

In this paper, we propose an algorithm for extracting text regions from images in spam-mails. The Color Layer-Based Text Extraction(CLTE) algorithm divides the input image into eight planes as color layers. It extracts connected components on the eight planes, and then classifies them into either text regions or non-text. We also propose an algorithm to recover damaged text strokes in Korean text images. There are two types of damaged strokes: (1) middle strokes such as ‘⌉' or ‘—' are deleted, and (2) the first and last strokes such as ‘∘' or ‘□' are filled with black pixels. An experiment with 200 spammail images shows that the proposed approach is more accurate than conventional methods by over 10%. [ABSTRACT FROM AUTHOR]

In this paper, we propose a keyword spotting system for Korean document images and compare the proposed system with an OCR-based document retrieval system. The system is composed of character segmentation, feature extraction for the query keyword, and word-to-word matching. In the character segmentation step, we propose an effective method to resolve the connection between adjacent characters. In the query creation step, feature vector for the query is constructed by a combination of the features for the constituent characters. In the matching step, word-to-word matching is applied based on a character matching. We demonstrated that the proposed keyword spotting system is more efficient than the OCR-based one to search a keyword on Korean document images, especially when the quality of documents is quite poor. [ABSTRACT FROM AUTHOR]