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 Saaraansh

What it does 🙌

We will build an easy-to-use web app that summarizes the lengthy legal documents into easy-to-understand terms and then convert them to the local language of the individual so that he/she is completely aware of what they are signing for.

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.

Features 👇

  • Summarise the lengthy and confusing legal documents into concise easy-to-understand bullet points.
  • Creating an OCR system to enable users to directly upload images of the document and get the summary.
  • Convert the summary to regional Language.
  • Create a sentiment analysis of the document to detect any unfair or fraudulent terms.
  • Create a mobile-friendly application for the same

How we’ll build 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. For training our model we used our own image as the train data and tested it in different settings.
  4. We will implement the Web App using cutting-edge web technologies like NextJs as React framework, nextAuth for handling auth, and Tailwind CSS for rapid prototyping.

Novelty 🥇

There is no existing software to create accurate summarization text with translation features. Adapting from the different research papers on legal document summarization, we are adding 2 novel features on translating the summary to regional languages and text to voice conversion for better understanding.

Future Aspects ✔

➡ Improving the NLP Model to create the summary more precisely.

➡ Create a mobile application for the same.

➡ Adding more regional languages and adding text-to-speech for the summarized text.

Tech Stack ⚙

  • NEXTjs
  • Tailwind CSS
  • React-Hook-Forms
  • SWR
  • HuggingFace
  • Web Browser API
  • Google API
  • Python
  • Tensorflow.js
  • Deployed on Vercel

The team

Built With

  • google
  • huggingface
  • nextjs
  • python
  • react-hook-forms
  • tailwind
  • tensorflow.js
  • vercel
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