TY - GEN
T1 - Text region extraction and text segmentation on camera-captured document style images
AU - Song, Y. J.
AU - Kim, K. C.
AU - Choi, Y. W.
AU - Byun, H. R.
AU - Kim, S. H.
AU - Chi, S. Y.
AU - Jang, D. K.
AU - Chung, Y. K.
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - In this paper, we propose a text extraction method from camera-captured document style images and propose a text segmentation method based on a color clustering method. The proposed extraction method detects text regions from the images using two low-level image features and verifies the regions through a high-level text stroke feature. The two level features are combined hierarchically, The low-level features are intensity variation and color variance. And, we use text strokes as a high-level feature using multi-resolution wavelet transforms on local image areas. The stroke feature vector is an input to a SVM (Support Vector Machine) for verification, when needed. The proposed text segmentation method uses color clustering to the extracted text regions. We improved K-means clustering method and it selects K and initial seed values automatically. We tested the proposed methods with various document style images captured by three different cameras. We confirmed that the extraction rates are good enough to be used in real-life applications.
AB - In this paper, we propose a text extraction method from camera-captured document style images and propose a text segmentation method based on a color clustering method. The proposed extraction method detects text regions from the images using two low-level image features and verifies the regions through a high-level text stroke feature. The two level features are combined hierarchically, The low-level features are intensity variation and color variance. And, we use text strokes as a high-level feature using multi-resolution wavelet transforms on local image areas. The stroke feature vector is an input to a SVM (Support Vector Machine) for verification, when needed. The proposed text segmentation method uses color clustering to the extracted text regions. We improved K-means clustering method and it selects K and initial seed values automatically. We tested the proposed methods with various document style images captured by three different cameras. We confirmed that the extraction rates are good enough to be used in real-life applications.
UR - http://www.scopus.com/inward/record.url?scp=33947364382&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33947364382&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2005.234
DO - 10.1109/ICDAR.2005.234
M3 - Conference contribution
AN - SCOPUS:33947364382
SN - 0769524206
SN - 9780769524207
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 172
EP - 176
BT - Proceedings of the Eighth International Conference on Document Analysis and Recognition
T2 - 8th International Conference on Document Analysis and Recognition
Y2 - 31 August 2005 through 1 September 2005
ER -