scholarly journals Text Line Segmentation With Water Flow Algorithm Based on Power Function

2015 ◽  
Vol 66 (3) ◽  
pp. 132-141 ◽  
Author(s):  
Darko Brodić
Keyword(s):  
Power Function ◽  
Water Flow ◽  
Document Image ◽  
Text Line ◽  
Flow Function ◽  
Basic Algorithm ◽  
Process Stage ◽  
Flow Algorithm ◽  
Text Line Segmentation ◽  
Line Segmentation

Abstarct This manuscript proposes an extension to the water flow algorithm for text line segmentation. Basic algorithm assumes hypothetical water flows under few specified angles of the document image frame from left to right and vice versa. As a result, unwetted image regions that incorporate text are extracted. These regions are of the major importance for text line segmentation. The extension of the basic algorithm means modification of water flow function that creates the unwetted region. Hence, the linear water flow function used in the basic algorithm is changed with its power function counterpart. Extended method was tested, examined and evaluated under different text samples. Results are encouraging due to improving text line segmentation which is a key process stage.

scholarly journals Text Line Segmentation with the Algorithm Based on the Oriented Anisotropic Gaussian Kernel

2013 ◽  
Vol 64 (4) ◽  
pp. 238-243 ◽  
Author(s):  
Darko Brodić ◽  
Zoran N. Milivojević
Keyword(s):  
Comparative Analysis ◽  
Document Image ◽  
Gaussian Kernel ◽  
Connected Components ◽  
Text Line ◽  
Text Line Segmentation ◽  
Bounding Boxes ◽  
Line Segmentation

The paper presents the algorithm for text line segmentation based on the oriented anisotropic Gaussian kernel. Initially, the document image is split into connected components achieved by bounding boxes. These connected components are cleared from redundant fragments. Furthermore, the binary moments are applied to each of these connected components evaluating local text skewing. According to this information the orientation of the anisotropic Gaussian kernel is set. After the algorithm application the boundary growing areas around connected components are established. These areas are of major importance for the evaluation of text line segmentation. For testing purposes, the algorithm is evaluated under different text samples. Comparative analysis between algorithm with and without orientation based on the anisotropic Gaussian kernel is made. The results show the improvement in the domain of text line segmentation.

DATASET AND GROUND TRUTH FOR HANDWRITTEN TEXT IN FOUR DIFFERENT SCRIPTS

2012 ◽  
Vol 26 (04) ◽  
pp. 1253001 ◽  
Author(s):  
ALIREZA ALAEI ◽  
UMAPADA PAL ◽  
P. NAGABHUSHAN
Keyword(s):  
Ground Truth ◽  
Document Image ◽  
Text Line ◽  
Document Recognition ◽  
Handwritten Text ◽  
Latin Script ◽  
Handwritten Document ◽  
Text Line Segmentation ◽  
Line Segmentation ◽  
Content Information

In document image analysis (DIA) especially in handwritten document recognition, standard databases play significant roles for evaluating performances of algorithms and comparing results obtained by different groups of researchers. The field of DIA regard to Indo-Persian documents is still at its infancy compared to Latin script-based documents; as such standard datasets are not still available in literature. This paper is an effort towards alleviating this gap. In this paper, an unconstrained handwritten dataset containing documents of Persian, Bangla, Oriya and Kannada (PBOK) is introduced. The PBOK contains 707 text-pages written in four different languages (Persian, Bangla, Oriya and Kannada) by 436 individuals. Total number of text-lines, words/subwords and characters are 12,565, 104,541 and 423,980, respectively. In most documents of PBOK dataset contain either an overlapping or a touching text-lines. The average number of text-lines in text-pages of the PBOK dataset is 18. Two types of ground truths, based on pixels information and content information, are generated for the dataset. Because of such ground truths, the PBOK dataset can be utilized in many areas of document image processing e.g. text-line segmentation, word segmentation and word recognition. To provide an insight for other researches, recent text-line segmentation results on this dataset are also reported.

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