Research on Efficient Calligraphy Image Classification Based on Attention Enhancement

As a task in the digital preservation of calligraphy stone inscriptions, an invaluable cultural heritage, style classification faces prominent challenges: insufficient feature representation of single-channel rubbings, and difficulties in effectively capturing the complex strokes and spatial layouts inherent to calligraphic works. To tackle these issues, an efficient deep learning model integrated with the dual-path attention mechanism of Bottleneck Attention Module (BAM) is proposed in this paper, which is designed to achieve accurate and efficient classification of calligraphy styles. With the lightweight network EfficientNetB2 as its backbone, this model innovatively integrates the BAM. It realizes the channel-spatial collaborative attention in calligraphy analysis, with the weight of stroke structure features increased to over 85%. Through the synergistic effect of channel attention and spatial attention, the model’s ability to extract stroke structure and spatial layout features from calligraphy images is significantly enhanced. The experimental results on the stratified sampling dataset show that the model achieves an accuracy of 98.44% on the test set, a confusion matrix recall rate of 94.80%, an F1-score of 0.9675, a precision of 0.8690, and a macro-averaged Area Under the Curve (AUC) value of 0.9694. To further validate the effectiveness of the BAM module and the necessity of its dual-path design, we conducted a systematic ablation experiment analysis. The experiment used EfficientNet-B2 as the baseline model and sequentially compared the contributions of different attention mechanisms. The experimental results show that the method proposed in this paper balances efficiency and performance, and holds practical significance in fields such as ancient book authentication and calligraphy research.