Problem Statement

"Scan virtually anything in the textbook to discover relevant DIKSHA content. Encourage curiosity and learning. Context: The QR codes introduced in Energised Textbooks (ETBs) improved content discovery for students. It bridged the digital and real world by allowing digital content to be placed in a physical format.Currently, DIKSHA content is accessed in the real world through these ubiquitous QR codes placed in all textbooks. Problem Statement: Using certain algorithms and Natural Language Processing (NLP) capabilities, the team can create a plugin into DIKSHA that allows students to scan almost anything in the textbook, (e.g. images or diagrams) and fetches relevant DIKSHA content or content from relevant sources from internet. The idea is to mimic our natural and organic methods to question things and be curious. Tapping into natural curiosity; a student is encouraged to point the DIKSHA scanner at anything and everything in the textbook. Encouraging participatory learning and creating a seamless experience."

Proposed Solution

To develop an app which will enrich the experience of a regular student and tap into his curiosity to learn about new things.

This App detects text and images that a student may capture in the scanner and shows all the relevant objects in the frame. The object with the highest probability will be taken as the keyword and the student can search on Diksha or Google scholar or Wikipedia. This can be helpful for students who have a lack of Educational infrastructure but have curious minds.

As students ourselves when we faced any doubts or tried to learn new things we had limited access to the internet and similar to relevant sources. DIKSHA is a platform that is trying to bridge the gap between reach and relevance of knowledge. Seeing this live in action around school students around our college and the implementation of QR codes made us too accept this as our problem statement for SIH.

Uniqueness in the project

• Scalability : The model is scalable and only requires a large enough dataset to generate increasing accuracy.

• Keyword recognition : Keyword filtration is executed to help increase the accuracy

• Cost : The cost is minimal towards development of the model as the whole project is developed on Open source technologies which is inline with the tech stack of Diskha.