Unconstrained online handwritten Uyghur word recognition based on recurrent neural networks and connectionist temporal classification

Ibrayim, Mayire; Simayi, Wujiahematiti; Hamdulla, Askar

doi:10.1504/ijbm.2021.112216

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…At present, Uyghur recognition has relatively mature technologies in printing [38][39][40][41][42][43][44][45][46][47][48] and handwriting [49][50][51][52]. The main research teams include Xinjiang University, Tsinghua University, Xidian University and so on.…”

Section: Uyghur Recognition 231 Uyghur Printing and Handwriting Recog...mentioning

confidence: 99%

“…Zhang et al proposed an offline signature identification method based on texture feature fusion and classified and identifies signature images by training random forests [49]. The team of Professor Maire Ibrain combined RNN and CTC to build an end-to-end online Uyghur handwriting recognition system [50].…”

Section: Uyghur Recognition 231 Uyghur Printing and Handwriting Recog...mentioning

confidence: 99%

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Scene Uyghur Recognition Based on Visual Prediction Enhancement

Liu,

Kong,

et al. 2023

Sensors

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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.

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Section: Uyghur Recognition 231 Uyghur Printing and Handwriting Recog...mentioning

confidence: 99%

Section: Uyghur Recognition 231 Uyghur Printing and Handwriting Recog...mentioning

confidence: 99%

Scene Uyghur Recognition Based on Visual Prediction Enhancement

Liu,

Kong,

et al. 2023

Sensors

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“…Chen Yang [30] from Chengdu University of Technology improved the decoding order of CRNN algorithm to adapt the network to the right-to-left writing order of Uyghur. Prof. Mayge Ibrahim's team [31] combined RNN with CTC to construct an end-to-end online Uyghur handwriting recognition system. Tang Jing et al [32] made a detailed analysis of the application of CRNN and attention mechanism-based text recognition methods on Uyghur text recognition, and the proposed TRBGA model achieved accurate recognition of Uyghur print.…”

Section: B Uyghur Scene Text Recognitionmentioning

confidence: 99%

SUST and RUST: Two Datasets for Uyghur Scene Text Recognition

Fanjie,

Yaqi,

Miaomiao

et al. 2023

IEEE Access

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The main objective of scene text recognition is to recognize text in complex images and convert it into editable text. However, scene text recognition research has long been focused on English, and there is a lack of research on other small languages, such as Uyghur language. This paper conducts a series of studies on Uyghur text recognition in natural scenes, with the following main contributions:(1) To address the lack of Uyghur scene text recognition datasets, we established the Synthetic Uyghur Scene Text dataset SUST and the Real Uyghur Scene Text dataset RUST, and we augmented RUST with STR-Aug. (2) The contemporary existing STR models are selected to conduct experiments on the dataset proposed in this paper, through which we search for the model structure suitable for Uyghur scene text recognition. (3) According to the characteristics of Uyghur text, this paper designs a lightweight Uyghur text recognition model named LUSTR, which takes into account both lightweight and accuracy, and achieves good performance in the Uyghur scene text recognition dataset proposed in this paper. The SUST and RUST are now publicly available at https://github.com/kongfnajie/SUST-and-RUST-datasets-for-Uyghur-STR.

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“…Currently, research on Uyghur language text recognition primarily focuses on printed and handwritten text recognition [23][24][25][26]. However, there is limited research on scene text recognition for the Uyghur language.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Encoding Method for Scene Text Recognition in Low Linguistic Resources: The Uyghur Language Case Study

Xu,

Zhang,

et al. 2024

Applied Sciences

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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%.

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Unconstrained online handwritten Uyghur word recognition based on recurrent neural networks and connectionist temporal classification

Cited by 6 publications

References 13 publications

Scene Uyghur Recognition Based on Visual Prediction Enhancement

Scene Uyghur Recognition Based on Visual Prediction Enhancement

SUST and RUST: Two Datasets for Uyghur Scene Text Recognition

Collaborative Encoding Method for Scene Text Recognition in Low Linguistic Resources: The Uyghur Language Case Study

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