Based on K-Net adhesive syllable segmentation for historical Tibetan document images
jingxiong zhou, Yusheng Hao, Weilan Wang, Qiaoqiao Li · 2024
Historical Tibetan document hold a significant place within surviving ancient literature in China for their representation of Tibetan culture and history. All of these documents were handwritten and thus possess various problems such as adhesion, blurred handwriting, and background stains. Segmentation of syllables from the text is a crucial step in analyzing images of Historical Tibetan document that must be completed prior to syllable recognition. Syllable segmentation is challenged by pre-syllable and post-syllable stroke sticking, tsheg sticking, and syllable stroke sticking. In this paper, we enhance the K-Net model to improve its efficacy in segmenting syllables in Historical Tibetan document texts. The main work includes: (1) to better classify the sticking syllables, we modify the backbone network to ResNeXt; (2) before entering into the kernel learning, we convert the feature mask and the segmentation mask into a convolution to increase the correct rate of mask prediction; (3) information proofreading in each step of kernel updating, and the mask prediction obtained from kernel learning is convolved with the feature mask to obtain a new mask prediction with higher correct rate; and (4) streamlining part of the instance segmentation code to lighten the network model. The experimental results demonstrate that the suggested technique can solve the syllable segmentation issue of syllable-to-syllable stroke sticking and tsheg-to-syllable stroke sticking proficiently. Consequently, syllable segmentation of mAcc attains 95.66%, and mIoU attains 76.59% for the Historical Tibetan document.