TY - GEN
T1 - Text Line Extraction Using Fully Convolutional Network and Energy Minimization
AU - Barakat, Berat Kurar
AU - Droby, Ahmad
AU - Alaasam, Reem
AU - Madi, Boraq
AU - Rabaev, Irina
AU - El-Sana, Jihad
N1 - Funding Information:
Acknowledgment. The authors would like to thank Gunes Cevik for annotating the ground truth. This work has been partially supported by the Frankel Center for Computer Science.
Funding Information:
The authors would like to thank Gunes Cevik for annotating the ground truth. This work has been partially supported by the Frankel Center for Computer Science.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/2/21
Y1 - 2021/2/21
N2 - Text lines are important parts of handwritten document images and easier to analyze by further applications. Despite recent progress in text line detection, text line extraction from a handwritten document remains an unsolved task. This paper proposes to use a fully convolutional network for text line detection and energy minimization for text line extraction. Detected text lines are represented by blob lines that strike through the text lines. These blob lines assist an energy function for text line extraction. The detection stage can locate arbitrarily oriented text lines. Furthermore, the extraction stage is capable of finding out the pixels of text lines with various heights and interline proximity independent of their orientations. Besides, it can finely split the touching and overlapping text lines without an orientation assumption. We evaluate the proposed method on VML-AHTE, VML-MOC, and Diva-HisDB datasets. The VML-AHTE dataset contains overlapping, touching and close text lines with rich diacritics. The VML-MOC dataset is very challenging by its multiply oriented and skewed text lines. The Diva-HisDB dataset exhibits distinct text line heights and touching text lines. The results demonstrate the effectiveness of the method despite various types of challenges, yet using the same parameters in all the experiments.
AB - Text lines are important parts of handwritten document images and easier to analyze by further applications. Despite recent progress in text line detection, text line extraction from a handwritten document remains an unsolved task. This paper proposes to use a fully convolutional network for text line detection and energy minimization for text line extraction. Detected text lines are represented by blob lines that strike through the text lines. These blob lines assist an energy function for text line extraction. The detection stage can locate arbitrarily oriented text lines. Furthermore, the extraction stage is capable of finding out the pixels of text lines with various heights and interline proximity independent of their orientations. Besides, it can finely split the touching and overlapping text lines without an orientation assumption. We evaluate the proposed method on VML-AHTE, VML-MOC, and Diva-HisDB datasets. The VML-AHTE dataset contains overlapping, touching and close text lines with rich diacritics. The VML-MOC dataset is very challenging by its multiply oriented and skewed text lines. The Diva-HisDB dataset exhibits distinct text line heights and touching text lines. The results demonstrate the effectiveness of the method despite various types of challenges, yet using the same parameters in all the experiments.
KW - Handwritten document
KW - Historical documents analysis
KW - Text line detection
KW - Text line extraction
KW - Text line segmentation
UR - http://www.scopus.com/inward/record.url?scp=85104465010&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-68787-8_9
DO - 10.1007/978-3-030-68787-8_9
M3 - Conference contribution
SN - 9783030687861
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 126
EP - 140
BT - Pattern Recognition. ICPR International Workshops and Challenges
A2 - Del Bimbo, Alberto
A2 - Cucchiara, Rita
A2 - Sclaroff, Stan
A2 - Farinella, Giovanni Maria
A2 - Mei, Tao
A2 - Bertini, Marco
A2 - Escalante, Hugo Jair
A2 - Vezzani, Roberto
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 25th International Conference on Pattern Recognition Workshops, ICPR 2020
Y2 - 10 January 2021 through 11 January 2021
ER -