sroie-layoutlmv3

sroie-layoutlmv3

microsoft/layoutlmv3-base fine-tuned for key-information extraction on the SROIE 2019 dataset (Malaysian receipts). Extracts company, date, address, and total via BIO token classification.

• Base model: microsoft/layoutlmv3-base (~125M parameters)
• Task: token classification, 9 BIO labels (O + B-/I- for company/date/address/total)
• Training: 563 train / 63 validation SROIE receipts. AdamW, lr 5e-5, weight decay 0.01, linear warmup, max 12 epochs with early stopping (patience 3); best checkpoint at epoch 5 (validation loss 0.0248).

Results — SROIE test set (347 receipts)

Numbers reflect the full inference pipeline (OCR → model → post-processing, including a regex date fallback). Address/company exact-F1 are bounded by OCR noise in both the input and the SROIE ground truth; fuzzy-F1 is the more representative figure for those fields.

Usage

This model expects pre-computed OCR words and normalized boxes (apply_ocr=False) plus the page image. The full pipeline and an interactive demo are available here:

• Demo: https://huggingface.co/spaces/Tanishq71/Receipt-Entity-Extractor
• Code: https://github.com/Tanishqarya17/Receipt-Entity-Extractor

Limitations

Specialized to SROIE-style receipts. Zero-shot transfer to other schemas/languages degrades sharply (near-zero macro-F1 on Indonesian CORD; 0.38 fuzzy macro on English WildReceipt). Date fields transfer best; structured fields like address and company do not. Not validated for production use.