Research on Tibetan complex background text recognition method based on lightweight network

doi:10.11996/JG.j.2095-302X.2026030492

Abstract

Abstract:

The recognition of Tibetan characters in complex backgrounds is one of the important research topics in the field of Tibetan OCR (Optical character Recognition). However, traditional deep learning models often have excessive parameter counts and are difficult to deploy, affecting the efficiency of Tibetan character recognition. To address the above problems, a Tibetan character recognition model for complex backgrounds based on the lightweight MobileNetV3-Global was proposed. The global attention mechanism (Global Context Block, GCB) was introduced into the last layer of the baseline model MobileNetV3. While maintaining the computational complexity of the network model, the global attention mechanism was used to perform global modeling on the line-text images of Tibetan characters in complex backgrounds, thereby facilitating feature extraction. Experimental results demonstrated that the improved lightweight MobileNetV3-Global model achieved high accuracy for Tibetan character recognition in complex backgrounds. In this study, tests were conducted on two types of datasets, namely natural scenes and streaming media. The accuracy rates of the model on these datasets reached 97.40% and 96.15%, respectively, and the model size was only 6.05 MB, indicating high practicality and deployment potential.

Key words: Tibetan character recognition, MobileNetV3, global context block, feature extraction, lightweight

CLC Number:

WANG Yuening, CAIRANG Dangzhi, ZAN Rong, MENG Lei. Research on Tibetan complex background text recognition method based on lightweight network[J]. Journal of Graphics, 2026, 47(3): 492-499.

Figures/Tables 13

Fig. 1 The overall structure of the improved MobileNetV3-Global Context Block model

Fig. 2 Structure of the Residual Unit

Fig. 3 Structure of the Global Context Block

Fig. 4 Examples of samples from two types of tibetan character recognition datasets ((a) Natural scene; (b) Streaming media)

Fig. 5 Comparison of model sizes with other methods

Table 1 Comparative experiments with other methods

方法	识别对象	模型	准确率/%	模型大小/MB
文献[1]	自然场景文本	PFRE	80.23	—
文献[5]		CNN + Attention	88.49	16
文献[6]		CNN-Bi-LSTM	90.55	41.2
文献[12]		CRNN+CTC	93.24	—
文献[7]	流媒体文本	CRNN	78.90	—
本文	自然场景文本	MobileNetV3-Gobal	97.40	6.05
本文	流媒体文本	MobileNetV3-Gobal	96.15	6.05

Fig. 6 Distribution of text image dimensions in the Tibetan - Chinese character recognition dataset ((a) Natural scene text recognition with fixed height; (b) Streaming media text recognition with variable height)

Fig. 7 Experimental ablation of global attention mechanism on two datasets ((a) Natural scene datasets; (b) Streaming media datasets)

Fig. 8 Accuracy changes of the training set for Tibetan text recognition in natural scenes

Fig. 9 Normalized edit distance in Tibetan text recognition under natural scenes

Fig. 10 Accuracy changes of the training set for Tibetan text recognition in natural scenes

Fig. 11 Normalized edit distance in Tibetan text recognition for streaming media

Fig. 12 Example of recognition results for two types of Tibetan characters ((a) Natural scenes; (b) Streaming media)

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