.png){kind=icon}
.png){kind=icon}
Return
Edge-Channel Aggregation Network and Two-Stage Fine Tuning Scheme for Handwritten Dongba Character Recognition
.png){kind=icon}
OA
AI
X
Y
X
Y
DOI：10.1049/cit2.70161.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Handwritten Dongba Character Recognition (HDCR) contains a large number of visually similar characters with subtle and fragile edge cues, posing severe challenges to feature learning. To address this issue, an Edge Channel Aggregation Network (EdgeCANet) model is proposed. The core of EdgeCANet is the Edge Channel Attention Aggregation (ECAA) block, which integrates the Edge-Guided Enhancement Module (EGEM) for edge-aware spatial refinement and the Channel Texture Recalibration Module (CTRM) for channel-wise texture recalibration. Besides, the two-stage misclassified sample weight redistribution fine-tuning scheme based on Exponential Moving Average (EMA) algorithm is proposed to stabilise parameter updates, and to emphasise difficult and highly similar classes. The recognition accuracy of EdgeCANet significantly outperforms state-of-the-art models across the constructed high-similarity datasets HS-DB and HS-S20 K, as well as the public DB1404, OBC306, Sketch-20 K, and ImageNet-Sketch datasets. The results validate that EdgeCANet effectively enhances recognition accuracy for highly similar handwritten characters and ancient minority-script characters, offering more promising applicability for related recognition tasks.
Keywords:
channel attention mechanism
Dongba character recognition
edge attention mechanism
exponential moving average
fine tuning
image recognition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.3
Papers:
649
Citations:
2.4K
Organization
Cited Papers