Text Line Segmentation in Handwritten Documents with Generative Adversarial Networks

Author(s):  
Ali Alper Demir ◽  
Ibrahim OzSeker ◽  
Ufuk Ozkaya
2017 ◽  
Vol 2 (2) ◽  
pp. 60
Author(s):  
Erick Paulus ◽  
Mira Suryani ◽  
Setiawan Hadi ◽  
Akik Hidayat

The variety of image quality of old Sundanese documents can be a real challenge for the process of text line segmentation. This paper describes the results of the investigation of two text line segmentation methods against several collections of Sunda document images, ie projection profile method and Seam Carving method. The deep investigation is done on handwritten documents written on lontar and paper media. The comparative experimental study was used as an investigative methodology in this study. Both methods is tested their performance capability on colored images and binary images using the evaluation matrix provided in handwriting segmentation competition ICDAR 2013. Experimental results show that projection profile method can work optimally on binary image and the type of writing is relatively horizontal. While the Seam Carving method is able to segment the lines in a non-linear manner and produce performance above 80%. With the added of binarization process in the pre-processing stage, the performance of Seam Carving method can increase up to 99% and the number of segmented lines is close to the number of groundtruth lines.


2006 ◽  
Author(s):  
Yi Li ◽  
Yefeng Zheng ◽  
David Doermann ◽  
Stefan Jaeger

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