farhamu
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bart-base-receipt-parser-1

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
+language:
+- en
+- id
+- my
+license: apache-2.0
+tags:
+- receipt-parsing
+- information-extraction
+- bart
+- ocr
+- text-to-text
+datasets:
+- dhiaznaidi/receiptdatasetssd300v2
 library_name: transformers
+pipeline-tag:
 ---
+# BART-base Receipt Parser
+## Model Description
+This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for receipt parsing tasks. The model is trained to extract key information from receipt text, specifically:
+- **Date**: Transaction date from the receipt
+- **Company Name**: Name of the merchant/store
+- **Total Amount**: Final amount paid
+## Dataset
+The model was trained using the [Receipt Dataset SSD300 V2](https://www.kaggle.com/datasets/dhiaznaidi/receiptdatasetssd300v2) from Kaggle, which contains receipt images with corresponding labels.
+## Data Processing Pipeline
+1. **OCR Processing**: All receipt images from the dataset were processed using [EasyOCR](https://github.com/JaidedAI/EasyOCR) to extract raw text
+2. **Input-Output Mapping**: The extracted OCR text serves as input, while the labeled data from the Kaggle dataset serves as the target output
+3. **Fine-tuning**: Supervised fine-tuning was performed on the facebook/bart-base model
+## Usage
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+# Load model and tokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained("fmuhammadf/bart-base-receipt-parser-v1")
+tokenizer = AutoTokenizer.from_pretrained("fmuhammadf/bart-base-receipt-parser-v1")
+# Example usage
+receipt_text = """
+SUPERMARKET ABC
+123 Main Street
+City, State 12345
+Date: 2024-01-15
+Item 1: $5.99
+Item 2: $3.50
+Tax: $0.76
+Total: $10.25
+Thank you for shopping!
+"""
+# Tokenize input
+inputs = tokenizer(receipt_text, return_tensors="pt", max_length=512, truncation=True, padding=True)
+# Generate output
+outputs = model.generate(
+    **inputs,
+    max_length=150,
+    num_beams=4,
+    early_stopping=True
+)
+# Decode result
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+## Expected Output Format
+The model outputs structured information in the following format:
+```
+Date: [extracted_date]
+Company Name: [extracted_company_name]
+Total Amount: [extracted_total_amount]
+```
 ## Training Details
+- **Base Model**: facebook/bart-base
+- **Task**: Text-to-Text Generation (Receipt Information Extraction)
+- **Training Data**: OCR-processed receipt text with labeled ground truth
+- **Data Source**: [Receipt Dataset SSD300 V2](https://www.kaggle.com/datasets/dhiaznaidi/receiptdatasetssd300v2)
+- **OCR Tool**: EasyOCR
+## Limitations
+- Performance may vary depending on OCR quality
+- Trained specifically on the format and style of receipts in the training dataset
+- May require additional fine-tuning for receipts with significantly different formats or languages
+## Use Cases
+- Automated receipt processing for expense management
+- Financial document digitization
+- Retail analytics and data extraction
+- Accounting automation
+## Citation
+If you use this model, please cite the original dataset:
+```bibtex
+@dataset{dhiaznaidi2024receipt,
+  title={Receipt Dataset SSD300 V2},
+  author={Dhiaz Naidi},
+  year={2024},
+  url={https://www.kaggle.com/datasets/dhiaznaidi/receiptdatasetssd300v2}
+}
+```