Papers › Attention based Writer Independent Handwriting Verification
Attention based Writer Independent Handwriting Verification
Mohammad Abuzar Shaikh, Tiehang Duan, Mihir Chauhan, Sargur Srihari
The task of writer verification is to provide a likelihood score for whether the queried and known handwritten image samples belong to the same writer or not. Such a task calls for the neural network to make it's outcome interpretable, i.e. provide a view into the network's decision making process. We implement and integrate cross-attention and soft-attention mechanisms to capture the highly correlated and salient points in feature space of 2D inputs. The attention maps serve as an explanation premise for the network's output likelihood score. The attention mechanism also allows the network to focus more on relevant areas of the input, thus improving the classification performance. Our proposed approach achieves a precision of 86\% for detecting intra-writer cases in CEDAR cursive "AND" dataset. Furthermore, we generate meaningful explanations for the provided decision by extracting attention maps from multiple levels of the network.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Handwriting Verification | AND Dataset | Siamese_MHCA_SA | Average F1 | 0.81 | #1 of 1 | Archive leaderboard | report |
| Handwriting Verification | CEDAR Signature | Siamese_MultiHeadCrossAttention_SoftAttention (Siamese_MHCA_SA) | FAR | 5.7 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: 3D SA
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