Abstract:Automated cheque processing requires localising key fields (date, legal amount, IFSC code, account number, signature, and payee name)
before any recognition step. The IDRBT Cheque Image Dataset is, to our knowledge, the only public collection of Indian bank cheques, but it
ships without field annotations and with no stated licence, so its redistribution terms are unclear. We address both limitations. First,
we release six-field bounding-box annotations for all 112 cheques in the dataset, distributed annotations-only and keyed to the original
filenames so that the IDRBT redistribution terms are respected. Second, we release 295 fully redistributable synthetic cheque images
produced by a cut-paste pipeline that composites annotated field regions from real cheques onto content-erased, bank-specific canvas
templates; because patches are pasted at their source coordinates, annotations carry forward unchanged. Third, we provide a ResNet-50
direct-regression baseline that predicts all six fields in a single forward pass, and use it for a controlled test of the synthetic data.
The test is sobering: because cheque layouts are rigid, a no-learning baseline that simply predicts each field's mean training box already
reaches 0.691 mean IoU and 80% accuracy at IoU >= 0.5, and once seed variance and training compute are accounted for, the cut-paste
synthetic data yields no measurable improvement over real data alone (an equal-compute real-only model matches or beats the
synthetic-augmented model on every aggregate metric). We report this negative result in full, since it cautions against assuming
appearance-only augmentation helps fixed-layout documents and points instead to layout-varying synthesis. The annotations and synthetic
images are released as reusable resources on the Hugging Face Hub under permissive licences.
From: Jaganadh Gopinadhan [view email]
[v1]
Sun, 14 Jun 2026 02:59:51 UTC (1,132 KB)
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