Segmentation and Recognition for Historical Tibetan Document Images

Ma, Li; Long, Congjun; Duan, Lijuan; Zhang, Xiqun; Li, Yanxing; Zhao, Quanchao

doi:10.1109/access.2020.2975023

Cited by 29 publications

(9 citation statements)

References 28 publications

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“…Therefore, "gamma transformation or intensity transformation operations" is used to eliminate these effects, in such way to improve image quality and to achieve light balance. Accordingly, Ma and et al [19] used equation 1 to calculate the document:…”

Section: A Document Image Preprocessingmentioning

confidence: 99%

Arabic Documents Information Retrieval for Printed, Handwritten, and Calligraphy Image

et al. 2021

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This paper presents a new computational backend model that support Arabic document information retrieval (ADIR) as a dataset and OCR services. Therefore, different services that support document analysis, retrieving, processing including dataset preparation, and recognition will be discussed. Consequently, ADIR services provide general functions of the Arabic OCR to compose large number of other services in the OCR domain. Furthermore, the proposed work can provide accessing different methods of document layout analysis with a platform where they can share and handle such methods (services) without any setup requirements. One of the used datasets composed from 16,800 Arabic letters written by 60 writers. Each writer wrote each letter from Alif to Ya 10 times in two forms. The forms were scanned at 3000DPI resolution and is segmented in two sets: training set with 13,440 letters for 48 images per class label, and testing set with 3,360 letters to 120 images per class label Convolutional neural network (CNN) is used and adapted for Arabic handwritten letters classification. In an experimental test, we showed that our results outperform 100% classification accuracy rate on testing images. Therefore, the ADIR services provide a "service description", which include an interface and a server's URL. The interface allows communication process between clients and services. Although, in this article we evaluate IR results and compared with respect to corrected equivalent.

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Section: A Document Image Preprocessingmentioning

confidence: 99%

Arabic Documents Information Retrieval for Printed, Handwritten, and Calligraphy Image

et al. 2021

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“…For documents with a more complex layout, advanced approaches have been proposed. Ma et al used block projection to divide images into text, line, and frames, and then utilized a graph model-based text line segmentation method to address the problem of touching strokes [15]. Wang et al pointed out that allocating connected components rather than pixels to the text line might reduce the noise and thus achieved finer performance [16].…”

Section: Related Workmentioning

confidence: 99%

Accurate Fine-grained Layout Analysis for the Historical Tibetan Document Based on the Instance Segmentation

Zhao,

Wang,

Cai

et al. 2021

Preprint

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Accurate layout analysis without subsequent text-line segmentation remains an ongoing challenge, especially when facing the Kangyur, a kind of historical Tibetan document featuring considerable touching components and mottled background. Aiming at identifying different regions in document images, layout analysis is indispensable for subsequent procedures such as character recognition. However, there was only a little research being carried out to perform line-level layout analysis which failed to deal with the Kangyur. To obtain the optimal results, a fine-grained sub-line level layout analysis approach is presented. Firstly, we introduced an accelerated method to build the dataset which is dynamic and reliable. Secondly, enhancement had been made to the SOLOv2 according to the characteristics of the Kangyur. Then, we fed the enhanced SOLOv2 with the prepared annotation file during the training phase. Once the network is trained, instances of the text line, sentence, and titles can be segmented and identified during the inference stage. The experimental results show that the proposed method delivers a decent 72.7% AP on our dataset. In general, this preliminary research provides insights into the fine-grained sub-line level layout analysis and testifies the SOLOv2-based approaches. We also believe that the proposed methods can be adopted on other language documents with various layouts.

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“…From 1991, Kojima et al [1]- [3] studied the recognition of Tibetan documents in woodcuts. Since 2010, researchers have carried out relevant studies on image preprocessing [4] [5], layout analysis [6] [8], text line segmentation [9]- [12], character segmentation [13] [14], dataset construction [15] [16], character recognition [17] [18] and other aspects of historical Tibetan documents.…”

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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Segmentation and Recognition for Historical Tibetan Document Images

Cited by 29 publications

References 28 publications

Arabic Documents Information Retrieval for Printed, Handwritten, and Calligraphy Image

Arabic Documents Information Retrieval for Printed, Handwritten, and Calligraphy Image

Accurate Fine-grained Layout Analysis for the Historical Tibetan Document Based on the Instance Segmentation

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

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