Delivery Receipt Extraction Platform

An AI document pipeline that turns bulk delivery receipt PDFs into clean, validated CSV files for a logistics app.

platform

Web Application, AI Development

team

1 Member

location

United States

industries

Logistics

platform

Web Application, AI Development

team

1 Member

location

United States

industries

Logistics

Project Background

The Delivery Receipt Extraction Platform is an AI document automation system that turns bulk delivery receipt PDFs into structured CSV files for the client's logistics application. It combines OCR, AI data extraction, barcode recognition and automated validation to handle high volumes of shipment paperwork.

Users upload PDF files with hundreds or thousands of receipts. The platform extracts recipient and shipment details, validates the records, removes duplicates and produces standard CSV reports to download.

Business Needs

The client needed to replace slow, error-prone manual data entry with automation that could process thousands of delivery receipts. Key requirements:

Process large, multi-page PDF files
Extract and normalize recipient and shipment details
Detect single and multiple barcodes on each receipt
Handle varying layouts and low-quality scans
Validate the data and remove duplicate records
Produce CSV files that the client's logistics application accepts
Show processing status, log errors and retry failed pages

Tech stacks

Python

For Backend

Streamlit

For Frontend

Open AI

For API

OpenCV

For AI / ML

Tesseract OCR

For AI / ML

pandas

For Backend

DigitalOcean

For Cloud Services

Service Provided

Document Data Extraction (OCR)

AI/LLM Integration

Back-End & API Development

Project Challenges

Inconsistent layouts: Field positions, spacing and optional details changed from receipt to receipt, and some pages held several shipments and many barcodes.
OCR quality: Low-resolution, blurry, cropped and rotated scans needed image preprocessing before text recognition was reliable.
Multiple barcodes per page: Each barcode had to be read and linked to the correct shipment record.
Address parsing: Addresses came in different formats, with and without suite or apartment numbers, and had to be split into clean fields.
High-volume batches: Large PDFs needed page-by-page processing with progress tracking, error handling and retries.
Duplicates and validation: Repeated receipts and incomplete or inconsistent values had to be caught before the CSV was built.

Strategy and Solutions

Bulk PDF upload through a Streamlit web interface
OCR pipeline that splits PDFs into pages, converts them to images and cleans them with OpenCV
AI field extraction with OpenAI models for recipients, addresses, delivery notes and shipment details
Address parsing for name, phone, city, state, ZIP and suite or apartment numbers
Barcode detection with pyzbar plus OCR checks for single and multiple barcodes
Duplicate filtering and mandatory field validation with pandas
CSV export in the format the client's logistics application expects
Failed-page flagging and retries
Processing dashboard with page status, errors and CSV downloads
Detailed logs for OCR, parsing and extraction issues

Results & Impact

Less manual data entry: staff no longer type receipt details into spreadsheets by hand
Consistent data across different receipt formats through OCR, AI parsing and validation
Barcode-heavy documents, including receipts with several barcodes, processed automatically
Bulk PDFs turned into structured CSV files ready for the logistics application
Reliable large batches with status tracking, error logs and failed-page retries

Delivery Receipt Extraction Platform results and impact

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