Receipt Analyser

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  VS Code folders and files

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  Website Dashboard

Inspiration

As a B.Tech CSE (AI & ML) student with a strong interest in Data Science, Artificial Intelligence, and Generative AI, I often look for opportunities to apply AI to real-world problems. One recurring issue I noticed was the difficulty of organizing and analyzing receipts for budgeting, reimbursements, or record-keeping. Receipts come in many formats, are often unclear, and extracting meaningful information manually is time-consuming. This motivated me to create a system that uses AI and OCR to automate the process and make receipt data accessible and structured.

What it does

Receipt Analyser is a web application that allows users to upload receipt images and automatically extract useful information such as vendor name, items purchased, prices, totals, and dates. The system processes the image, performs OCR-based text extraction, parses the unstructured text, and presents the results in a clean and organized format. The goal is to simplify expense tracking and reduce the need for manual entry.

How we built it

The project was developed using the following technologies:

Next.js and React for the frontend interface

OCR and text extraction models to process receipt images

Custom parsing logic to identify items, totals, and key fields

API routes for processing uploaded images

Vercel for fast and reliable deployment

The architecture focuses on being lightweight, efficient, and suitable for real-time use.

Challenges we ran into

Some of the main challenges included:

Handling low-quality or skewed receipt images

Parsing text from different receipt formats and inconsistent layouts

Managing noise, errors, and unwanted characters in OCR output

Extracting reliable totals, dates, and item details from unstructured text

Designing a user-friendly interface that works well with varied input data

Addressing these challenges helped refine the system and improve its robustness.

Accomplishments that we're proud of

Successfully implementing an OCR-based pipeline that works on a wide range of receipt images

Building a clean, responsive, and simple user interface

Achieving reliable extraction of key fields such as totals, items, and vendor information

Deploying a fully functional application accessible through the web

The project demonstrates practical application of AI and OCR in solving everyday problems.

What we learned

Through this project, I gained deeper experience with:

OCR techniques and preprocessing for text extraction

Handling unstructured and noisy text data

Designing user-friendly interfaces using modern frontend frameworks

Improving the accuracy and consistency of data parsing pipelines

Deploying production-ready applications

It also strengthened my understanding of how AI can automate repetitive tasks in meaningful ways.

What's next for Receipt Analyser

Future improvements planned for the project include:

Adding expense categorization using AI

Providing CSV or Excel export for extracted data

Building a dashboard for analytics and monthly spending insights

Supporting batch uploads for processing multiple receipts at once

Enhancing extraction accuracy with improved AI models

Extending support to receipts in multiple languages

Built With

• css
• google
• javascript
• next.js
• node.js
• ocr-based-extraction
• react
• tailwind-css
• typescript
• vercel