Y-Position based Myanmar Touching Character Segmentation and Sub-components based Character Classification

Zaw, Kyi Pyar; War, Nu

doi:10.1109/sera.2019.8886810

Cited by 3 publications

(1 citation statement)

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“…Tian et al [21] improved the method of Qi et al, taking into account the problem of character undersegmentation and using the improved K-means to solve the problem of touching character segmentation. Zaw and War [22] proposed a method based on character connected component analysis to complete the segmentation of Myanmar touching characters. Thongkanchorn et al [23] proposed a vertical and horizontal segmentation method based on a 4-direction depth-first search algorithm and completed the segmentation of Thai characters.…”

Section: Introductionmentioning

confidence: 99%

Character Detection and Segmentation of Historical Uchen Tibetan Documents in Complex Situations

et al. 2022

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Tibetan is a low-resource language, and Tibetan culture carried by historical Tibetan documents is an important part of Chinese civilization. The study of historical Tibetan documents is of great significance to the protection of Tibetan culture and the promotion of Chinese culture. Character segmentation is an important step in image analysis and recognition of historical Tibetan documents. However, the following three challenges prevent solving problems of character segmentation in historical Tibetan documents: 1) the text lines have different degrees of tilt and twist; 2) there are many complex situations such as overlapping, crossing, touching and breaking character strokes; and 3) these documents are written by different people with different stroke styles. To resolve these problems, we propose a character segmentation method based on key feature information for historical Tibetan documents. The proposed method consists of three parts: 1) projection and syllable point location information are used to shorten the text lines of historical Tibetan documents and establish a character block database; 2) the local baseline of the character block is detected by using the location information of syllable points or combined with horizontal projection and straight line detection, and the character block is divided into two areas above and below the baseline, and different segmentation methods are adopted; and 3) in view of the large difference in stroke styles, three stroke attribution distances are used to complete the attribution. The experimental results show that the method proposed in this paper can effectively solve the problem of character segmentation of historical Tibetan documents and achieve a better character segmentation effect, which also provides a reference for the relevant document character segmentation.

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Section: Introductionmentioning

confidence: 99%