Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- receipt
+- document-ai
+- layoutlmv3
+- token-classification
+- ner
+- information-extraction
+datasets:
+- custom-receipt-dataset
+metrics:
+- accuracy
+widget:
+- text: "STORE_NAME Date: 2024-01-01 Total: 25.99"
+---
+
+# LayoutLMv3 Receipt Parser
+
+A fine-tuned LayoutLMv3 model for extracting structured information from receipt images with **89.34% validation accuracy**.
+
+## Model Details
+
+- **Model**: `albertosei/layoutlmv3-receipt-parser`
+- **Architecture**: LayoutLMv3-base
+- **Task**: Token Classification (Named Entity Recognition)
+- **Languages**: English
+- **Training Data**: 1,426 receipt samples
+- **Validation**: 100 samples
+- **License**: Apache 2.0
+
+## Performance Metrics
+
+| Metric | Value |
+|--------|-------|
+| **Final Validation Accuracy** | **89.34%** |
+| Training Loss (Epoch 1) | 0.6824 |
+| Training Loss (Epoch 2) | 0.3278 |
+| Validation Accuracy (Epoch 1) | 83.49% |
+| Number of Entity Labels | 51 |
+
+## Entity Labels
+
+The model recognizes 25 entity types in BIO format:
+
+### Vendor Information
+- `vendor_name` - Store/business name
+- `vendor_address` - Physical address
+- `vendor_phone_number` - Contact number
+
+### Date & Time
+- `date` - Transaction date
+- `time` - Transaction time
+
+### Receipt Details
+- `receipt_id` - Receipt number/identifier
+- `currency` - Currency type
+
+### Financial Amounts
+- `total_amount` - Final total
+- `subtotal_amount` - Subtotal before tax
+- `tax_amount` - Tax amount
+- `service_charge_amount` - Service fees
+- `discount_amount` - Discounts applied
+- `tip_amount` - Tip/gratuity
+
+### Payment Information
+- `cash_paid_amount` - Cash payment
+- `change_amount` - Change returned
+- `credit_card_amount` - Credit card payment
+- `e_money_amount` - Electronic payment
+- `payment_method` - Payment type
+
+### Line Items
+- `line_item_name` - Product/service name
+- `line_item_quantity` - Quantity purchased
+- `line_item_unit_price` - Price per unit
+- `line_item_total_price` - Line item total
+- `line_item_discount_amount` - Item-level discount
+- `line_item_vat_status` - VAT information
+
+### Other
+- `other` - Miscellaneous information
+
+## Usage
+
+```python
+from transformers import AutoProcessor, AutoModelForTokenClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+processor = AutoProcessor.from_pretrained("albertosei/layoutlmv3-receipt-parser", apply_ocr=False)
+model = AutoModelForTokenClassification.from_pretrained("albertosei/layoutlmv3-receipt-parser")
+
+# Prepare inputs (requires external OCR for text and bounding boxes)
+image = Image.open("receipt.jpg").convert("RGB")
+words = ["STORE", "NAME", "Date:", "2024-01-01", "Total:", "25.99"]  # From OCR
+boxes = [[0, 0, 100, 20], [100, 0, 200, 20], [0, 20, 50, 40], 
+         [50, 20, 150, 40], [0, 40, 50, 60], [50, 40, 150, 60]]  # From OCR
+
+# Process and predict
+encoding = processor(image, words, boxes=boxes, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**encoding)
+    predictions = outputs.logits.argmax(-1).squeeze().tolist()
+
+# Convert to labels
+predicted_labels = [model.config.id2label[pred] for pred in predictions]