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\"The current volume presents a selection of 126 texts in Uyghur posted in public spaces, translated, and annotated for this book. The author started photographing Uyghur texts in 2008 at the time of the Beijing olympics and continued to do so during 2009, the year of the so-called \"Urumqi uprising\" of July 5. This event generated a stream of texts posted in public spaces that reflected the efforts made by the authorities to re-establish control. In the course of his travels in the years thereafter the author continued to add to the corpus of photographed Uyghur texts. At the same time he started collecting, as comprehensively as possible, various types of folders, brochures, handouts, and product wrappings with texts illustrating aspects of Uyghur culture and society. The texts, published here for the first time, are primary source materials documenting a wide variety of aspects of daily life of the Uyghurs in Shinjang. The implicit messages or explicit references contained in many of these texts give them significance as clues towards an understanding of the existential realities they reflect or illustrate.\"--Provided by publisher.

Aiming at the problems of Uyghur oblique deformation, character adhesion and character similarity in scene images, this paper proposes a scene Uyghur recognition model with enhanced visual prediction. First, the content-aware correction network TPS++ is used to perform feature-level correction for skewed text. Then, ABINet is used as the basic recognition network, and the U-Net structure in the vision model is improved to aggregate horizontal features, suppress multiple activation phenomena, better describe the spatial characteristics of character positions, and alleviate the problem of character adhesion. Finally, a visual masking semantic awareness (VMSA) module is added to guide the vision model to consider the language information in the visual space by masking the corresponding visual features on the attention map to obtain more accurate visual prediction. This module can not only alleviate the correction load of the language model, but also distinguish similar characters using the language information. The effectiveness of the improved method is verified by ablation experiments, and the model is compared with common scene text recognition methods and scene Uyghur recognition methods on the self-built scene Uyghur dataset.

The historical evolution of two designations for East Syriac Christians in China—taso (達娑, including its variants) and erke’ün (也里可溫)—from the Tang to the Yuan dynasty is examined. Analyses of historical records and Old Uighur Christian manuscripts reveal their usage patterns, referents, and historical development. Taso and its variants served as both self-referential and externally applied designations from West Asia to East Asia before and during the early Mongol–Yuan period. Erke’ün, initially an official title for East Syriac Church leaders under Mongol–Yuan rule, replaced Taso and expanded to denote Christians in general. This terminological shift reflects significant transformations in the community’s identity and institutional standing within China. The findings offer new perspectives on the transmission and adaptation of East Syriac Christianity in the Chinese context.

Pratītyasamutpāda, the doctrine of dependent origination, has a long history in Old Uyghur Buddhism. It was first articulated in the Early Old Uyghur Buddhist texts and is evident in the terminology of Maitrisimit and the Daśakarmapathāvadāna-mālā. The dependent origination is systematically illustrated in at least three Pratītyasamutpāda texts, one text with Brāhmī elements, and the other two in Dunhuang and Turfan prints. The latter two are discussed in detail in this paper. The Dunhuang print provides the most comprehensive demonstration of the Old Uyghur understanding of dependent origination. The structure of the text is largely consistent with the corresponding passages in the Abhidharmamahāvibhāṣaśāstra and other Abhidharma texts. The text offers a more comprehensive account than the Chinese text. The Turfan prints, which consist of four fragments, are derived from two distinct prints. Print U 4170 is an Abhidharma text, and it has parallels in the Abhidharmakośabhāṣya. It seems plausible to suggest that the print bearing the abbreviated titles Pratyitasamutpad in Old Uyghur and Buladi 布剌帝 in Chinese may have been translated from a Chinese text sharing the same or a similar Chinese name. However, as with the Dunhuang print, the Turfan prints may have been produced by the Old Uyghurs from some Abhidharma texts. The Dunhuang print and the Tufan prints are unique within the corpus of known Old Uyghur prints. These texts represent the first known printed examples of the Abhidharma tradition. Moreover, the illustration employed in the Dunhuang print is not known in other printed texts discovered in Dunhuang and Turfan, representing the first instance of such an illustration in printed form.

Current research on scene text recognition primarily focuses on languages with abundant linguistic resources, such as English and Chinese. In contrast, there is relatively limited research dedicated to low-resource languages. Advanced methods for scene text recognition often employ Transformer-based architectures. However, the performance of Transformer architectures is suboptimal when dealing with low-resource datasets. This paper proposes a Collaborative Encoding Method for Scene Text Recognition in the low-resource Uyghur language. The encoding framework comprises three main modules: the Filter module, the Dual-Branch Feature Extraction module, and the Dynamic Fusion module. The Filter module, consisting of a series of upsampling and downsampling operations, performs coarse-grained filtering on input images to reduce the impact of scene noise on the model, thereby obtaining more accurate feature information. The Dual-Branch Feature Extraction module adopts a parallel structure combining Transformer encoding and Convolutional Neural Network (CNN) encoding to capture local and global information. The Dynamic Fusion module employs an attention mechanism to dynamically merge the feature information obtained from the Transformer and CNN branches. To address the scarcity of real data for natural scene Uyghur text recognition, this paper conducted two rounds of data augmentation on a dataset of 7267 real images, resulting in 254,345 and 3,052,140 scene images, respectively. This process partially mitigated the issue of insufficient Uyghur language data, making low-resource scene text recognition research feasible. Experimental results demonstrate that the proposed collaborative encoding approach achieves outstanding performance. Compared to baseline methods, our collaborative encoding approach improves accuracy by 14.1%.

Uyghur text recognition faces several challenges in the field due to the scarcity of publicly available datasets and the intricate nature of the script characterized by strong ligatures and unique attributes. In this study, we propose a unified three-stage model for Uyghur language recognition. The model is developed using a self-constructed Uyghur text dataset, enabling evaluation of previous Uyghur text recognition modules as well as exploration of novel module combinations previously unapplied to Uyghur text recognition, including Convolutional Recurrent Neural Networks (CRNNs), Gated Recurrent Convolutional Neural Networks (GRCNNs), ConvNeXt, and attention mechanisms. Through a comprehensive analysis of the accuracy, time, normalized edit distance, and memory requirements of different module combinations on a consistent training and evaluation dataset, we identify the most suitable text recognition structure for Uyghur text. Subsequently, utilizing the proposed approach, we train the model weights and achieve optimal recognition of Uyghur text using the ConvNeXt+Bidirectional LSTM+attention mechanism structure, achieving a notable accuracy of 90.21%. These findings demonstrate the strong generalization and high precision exhibited by Uyghur text recognition based on the proposed model, thus establishing its potential practical applications in Uyghur text recognition.

Currently, most state-of-the-art scene text recognition methods are based on the Transformer architecture and rely on pre-trained large language models. However, these pre-trained models are primarily designed for resource-rich languages and exhibit limitations when applied to low-resource languages. We propose a Correlation-Guided Decoding Strategy for Low-Resource Uyghur Scene Text Recognition (CGDS). Specifically, (1) CGDS employs a hybrid encoding strategy that combines Convolutional Neural Network (CNN) and Transformer. This hybrid encoding effectively leverages the advantages of both methods: On one hand, the convolutional properties and shared weight mechanism of CNN allow for efficient extraction of local features, reducing dependency on large datasets and minimizing errors caused by similar characters. On the other hand, the global attention mechanism of Transformer captures longer-distance dependencies, enhancing the informational linkage between characters and thereby improving recognition accuracy. Finally, through a dynamic fusion method, the features from CNN and Transformer are dynamically integrated, adaptively allocating the weights of CNN and Transformer features during the model training process, thereby achieving a dynamic balance between local and global features. (2) To further enhance the feature extraction capabilities, we designed a Correlation-Guided Decoding (CGD) module. Unlike existing decoding strategies, we adopt a dual-decoder approach with the Transformer and CGD decoders. The role of the CGD decoder is to perform correlation calculations using the outputs from the Transformer decoder and the encoder to optimize the final recognition performance. At the same time, the CGD decoder can utilize the outputs from the Transformer decoder to provide semantic guidance for the feature extraction of the encoder, enabling the model to understand the semantic structure within the input data better. This dual-decoder strategy can better guide the model in extracting effective features, enhancing the model’s ability to learn internal language knowledge and more fully utilize the useful information in the input data. (3) We constructed two Uyghur scene text datasets named U1 and U2. Experimental results show that our method achieves superior performance in low-resource Uyghur scene text recognition compared to existing technologies. Specifically, CGDS improved accuracy by 50.2% on the U1 and 13.6% on the U2 and achieved an overall accuracy improvement of 15.9%.

This paper mainly discusses the use of mutual information (MI) and Support Vector Machines (SVMs) for Uyghur Web text classification and digital forensics process of web text categorization: automatic classification and identification, conversion and pretreatment of plain text based on encoding features of various existing Uyghur Web documents etc., introduces the pre-paratory work for Uyghur Web text encoding. Focusing on the non-Uyghur characters and stop words in the web texts filtering, we put forward a Multi-feature Space Normalized Mutual Information (M-FNMI) algorithm and replace MI between single feature and category with mutual information (MI) between input feature combination and category so as to extract more accurate feature words; finally, we classify features with support vector machine (SVM) algorithm. The experimental result shows that this scheme has a high precision of classification and can provide criterion for digital forensics with specific purpose.

This article recounts the history of Islamic school reform in Xinjiang, from its beginnings in the late 1890s to the crackdown on foreign teachers in Xinjiang schools at the end of World War I. Drawing on a wide range of Turkic-language sources and consular reports, it explores the nature of social and political contacts between the Muslims of Xinjiang and co-believers in the Russian and Ottoman Empires in the early twentieth century. It also seeks to situate Jadidism in Xinjiang in its distinctive late-Qing context, in which Islamic reform initiatives came to coincide with, and complement, the Qing empire's own steps towards Western-style schooling.

In the Mongol period, the Uighurs who settled around the Turfan region not only translated Chinese Buddhist works into the Uighur language, but also directly copied them in Chinese characters or composed original works with the combination of arbitrary quotations from Chinese works. The Insadi-Sūtra is such a work in question. The author of this paper succeeded in identifying two Chinese Buddhist texts written by Uighurs. They will help us better understand the background in which these Uighur–Chinese mixed texts came about.