AI Agent - Transportation Invoice Reader

AI Agent -
Transportation Invoice Reader

Industry

Logistics

Team size

5 people

Type

Web

Location

Germany

Timeline

12 weeks

Technologies

Java Spring Boot, VueJS, MySQL, AWS

Demo Account
Email: test123@yopmail.com Password: 123456@aA

Demo Account
Email: test123@yopmail.com
Password: 123456@aA

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ZOBA' Screens

Project Overview

This is an AI-based app for reading and extracting order shipment information. It analyzes, labels, and feeds the data into existing processing systems. ZOBA supports thousands of order formats, significantly reducing manual data entry.

Key Features

Convert the uploaded files into image format.
Utilize AI to classify the document types.
Apply AI to extract and label key information from the documents.
Perform a sanction check on the extracted data.
Transfer the processed information to the designated service provider.
...

Challenges

1. Low-Quality Input Documents: Documents are often blurry, skewed, or poorly captured, making it difficult for OCR to accurately recognize text.
2. Variety of Document Formats (Invoices, Delivery Notes, Itemized Lists, etc.): Each logistics partner or client may use different templates, making it difficult for a single model to accurately extract data across all formats.
3. Complex Table Extraction (Tabular Data): Tables containing product or cost information are often misread by OCR, resulting in broken rows or columns and inaccurate data.
4. Imperfect Extracted Data – Needs Verification: AI cannot guarantee 100% accuracy, which can lead to system errors if outputs aren't verified.

Solutions

Apply image pre-processing techniques such as: Sharpening and contrast enhancement , automatic deskewing (straightening the image), use libraries like OpenCV or PIL to standardize images before feeding them into the OCR process.
Develop a template classification model to identify the document type before extraction. Allow customizable training for enterprise-specific document templates.
Utilize specialized models for table extraction, such as Amazon Textract AnalyzeDocument.
Implement a human-in-the-loop confirmation interface that displays AI results and allows quick manual corrections.
Use confidence scores to highlight fields that require user verification.
Log corrected data to retrain and improve the model over time.

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