TY - GEN
T1 - Stroke-like pattern noise removal in binary document images
AU - Agrawal, Mudit
AU - Doermann, David
PY - 2011
Y1 - 2011
N2 - This paper presents a two-phased stroke-like pattern noise (SPN) removal algorithm for binary document images. The proposed approach aims at understanding script-independent prominent text component features using supervised classification as a first step. It then uses their cohesiveness and stroke-width properties to filter and associate smaller text components with them using an unsupervised classification technique. In order to perform text extraction, and hence noise removal, at diacritic-level, this divide-and-conquer technique does not assume the availability of accurate and large amounts of ground-truth data at component-level for training purposes. The method was tested on a collection of degraded and noisy, machine-printed and handwritten binary Arabic text documents. Results show pixel-level precision and recall of 86% and 90% respectively for noise-pixels.
AB - This paper presents a two-phased stroke-like pattern noise (SPN) removal algorithm for binary document images. The proposed approach aims at understanding script-independent prominent text component features using supervised classification as a first step. It then uses their cohesiveness and stroke-width properties to filter and associate smaller text components with them using an unsupervised classification technique. In order to perform text extraction, and hence noise removal, at diacritic-level, this divide-and-conquer technique does not assume the availability of accurate and large amounts of ground-truth data at component-level for training purposes. The method was tested on a collection of degraded and noisy, machine-printed and handwritten binary Arabic text documents. Results show pixel-level precision and recall of 86% and 90% respectively for noise-pixels.
KW - degraded ruled-line removal
KW - low-density languages
KW - noise
KW - salt-n-pepper
KW - speckle removal
KW - stroke-like pattern noise
UR - https://www.scopus.com/pages/publications/82355162893
U2 - 10.1109/ICDAR.2011.13
DO - 10.1109/ICDAR.2011.13
M3 - Conference contribution
AN - SCOPUS:82355162893
SN - 9780769545202
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 17
EP - 21
BT - Proceedings - 11th International Conference on Document Analysis and Recognition, ICDAR 2011
T2 - 11th International Conference on Document Analysis and Recognition, ICDAR 2011
Y2 - 18 September 2011 through 21 September 2011
ER -