Abstract
The paper presents the approach to the algorithm for text line segmentation based on the anisotropic Gaussian kernel. As a result of this algorithm the growing region around text is exploited. Furthermore, anisotropic Gaussian kernel is rotated to improve text line segmentation process. Text objects orientation is evaluated by binary moments. For test purposes algorithm is evaluated under different text samples. From the obtained results comparative analysis between algorithm with anisotropic and oriented Gaussian kernel is made. At the end, benefits of the extended approach are revealed. Ill. 7, bibl. 22, tabl. 5 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.118.2.1181
Highlights
Text line segmentation is a major step in a document analytic procedure
Processing of handwritten documents has been remained a key problem in optical character recognition (OCR) [2, 3]
Most text line segmentation methods are based on the assumptions that distance between neighboring text lines is sufficiently large and text lines are reasonably straight
Summary
Text line segmentation is a major step in a document analytic procedure. It is prerequisite for the valid optical character recognition (OCR) process. The text line segmentation and character recognition are dependent tasks as well [1]. There are a few successful techniques for printed text line segmentation. Most text line segmentation methods are based on the assumptions that distance between neighboring text lines is sufficiently large and text lines are reasonably straight. These assumptions are not always valid for handwritten documents. Text line segmentation is a leading challenge in OCR
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