The problem Saaraansh solves

Inspiration ✨

Around the world, millions of adults are unable to read or write, and therefore fall prey to the extremely confusing jargon of lengthy legal documents. India has the highest adult illiteracy rate in the world. According to the latest report published by UNESCO, there are 287 million illiterate adults in India—37 percent of the illiterate population in the entire world. Farmers, manual laborers, and the people below the poverty line don't have access to education, therefore do not understand the legal terms and even can't understand the language of the document which are generally in English, and fall trap to financial debts in many cases. Therefore, we present to you Saaransh

What it does 🙌

We built an easy-to-use web app that summarizes the lengthy legal documents into easy-to-understand terms and then converts them to the local language of the individual so that he/she is completely aware of what they are signing for by simpy uploading/taking a picture of the document.

The app works like this: The users can submit the documents they want to understand and the app uses an API to process the document and gives the user back a simplified and summarised document in their regional language.

This supplies the user with a much easier-to-understand document with which they can understand the broader terms of the document and can make a decision whether they should go ahead with the agreements or not.

How we built it 💡

1. The website UI/UX will be designed using Figma and then developed with Next.js, The React Framework and Tailwind CSS for UI, Tensorflow.js, Python, and GCP API to translate the summary to the regional language.
2. We’ll use the open-source framework of Hugging Face to create the document summarizer using Natural Language Processing.
3. We used OCR Tesseract.js to implement the text recognition feature.
4. We will implement the Web App using cutting-edge web technologies like NextJs as React framework.

Challenges we ran into

Challenges we ran into

1. Creating a feasible NLP text summarization model to integrate into the website.
2. Implementing OCR, we used tesseract.js to overcome this issue.
3. Integrating multiple languages for text translation.