Online Mongolian Handwriting Recognition Based on Encoder–Decoder Structure with Language Model

Fan, Daoerji; Sun, Yuxin; Wang, Zhixin; Peng, Yanjun

doi:10.3390/electronics12204194

Cited by 2 publications

(1 citation statement)

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“…On the MOLHW dataset, ref. [17] has carried out work on Mongolian handwriting identification, but no writer identification, and this paper will utilize this dataset to transform into corresponding offline images for offline research.…”

Section: Related Workmentioning

confidence: 99%

Offline Mongolian Handwriting Identification Based on Convolutional Neural Network

Sun,

Fan,

et al. 2023

Electronics

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Handwriting is a biometric behavioral characteristic with evident individual distinctiveness. With the rise of the deep learning trend and demands for forensic identification, handwriting identification has become one of the focal points of research in the field of pattern recognition. Research in handwriting identification for major global languages has matured. However, in China, there is limited attention in the field of writer identification for minority languages such as Mongolian, making it challenging to resolve criminal cases involving handwriting issues. This paper initiates an initial exploration of Mongolian handwriting identification by constructing a structurally simple convolutional neural network. This convolutional neural network, consisting of 12 convolution operations and designed for Mongolian handwriting identification, is referred to as MWInet-12. In this paper, the model evaluation experiments were conducted using a dataset comprising 156,372 samples contributed by 125 writers from the MOLHW dataset. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The final results of the experiments reveal impressive accuracy on the test set, achieving a top-1 accuracy of 89.60% and a top-5 accuracy of 97.53%. Furthermore, through comparative experiments involving Resnet50, Fragnet, GRRNN, VGG16, and VGG19 models, this paper establishes that the proposed model yields the most favorable results for Mongolian handwriting identification. The exploratory research on Mongolian handwriting identification in this paper contributes to increasing awareness of information processing for minority languages. It aids in advancing research on classifying writers of Mongolian historical texts and provides technical support for judicial authentication involving handwriting issues.

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Section: Related Workmentioning

confidence: 99%

Offline Mongolian Handwriting Identification Based on Convolutional Neural Network

Sun,

Fan,

et al. 2023

Electronics

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Integrating path signature and pen-tip trajectory features for online handwriting Yi text recognition

Xiao,

Chen,

et al. 2024

Herit Sci

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Recognizing online handwriting Yi text is crucial for recording and preserving Yi literature. However, the scarcity of online handwriting Yi text datasets has limited relevant research, impeding the process of Yi informatization. In this work, we use synthetic data to train models, and an Online Handwriting Yi Text Recognition Network (YTRN) is proposed, which extracts robust character features to address the gap between synthetic and real data. YTRN adeptly learns the spatial structure features from path signature feature maps and captures trajectory features from the pen-tip trajectories. Subsequently, an innovative adaptive feature fusion module integrates these two sets of features to yield more comprehensive and robust character representations. Experiments on our newly collected Yi-OLHWDB2.0 dataset demonstrate that our method outperforms previous approaches, achieving an impressive 95.67% accuracy. This highlights the model’s effectiveness in extracting comprehensive and robust features from path signature maps and pen-tip trajectories, significantly enhancing recognition accuracy and generalization.

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Online Mongolian Handwriting Recognition Based on Encoder–Decoder Structure with Language Model

Cited by 2 publications

References 24 publications

Offline Mongolian Handwriting Identification Based on Convolutional Neural Network

Offline Mongolian Handwriting Identification Based on Convolutional Neural Network

Integrating path signature and pen-tip trajectory features for online handwriting Yi text recognition

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