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NLP text-summarizer and virtual Board

dd/mm/yyyy

TEAM

K.HARSHITHA 1602-21-735-014

TARUN 1602-21-735-051

M.VAISHNAVI 1602-21-735-056

1

What is the problem statement

WHAT

Techstack

THE PROBLEM

THE PROBLEM

PROBLEM STATEMENT:

"In the context of enhancing collaboration, productivity, and information retrieval in a digital workspace, we aim to develop an integrated solution that leverages Natural Language Processing (NLP) and Computer Vision technologies. The primary objectives are as follows:

Text Summarizer using Transformer Model: Develop a robust NLP-based text summarization system powered by a state-of-the-art transformer model (Hugging face) to automatically condense lengthy textual content into concise summaries. This tool should be able to handle various types of textual data, including documents, articles, and reports.

Virtual Board with Computer Vision Canvas: Create a virtual collaboration platform with a computer vision-powered canvas that allows users to interact visually with digital content. The canvas should enable users to draw, annotate, and manipulate digital objects, making it a versatile tool for collaborative work, presentations, and brainstorming.

This problem statement clearly outlines the goals, challenges, and expected outcomes of the project, providing a roadmap for developing the NLP text summarizer and the virtual board using computer vision technology.

Tech stack

*Jupyter notebook

*Anaconda cmd

*NLP(trabsformers)

*Open_cv

*canvas(computer vision)

*Python

*Git(reposit)

2

WHY

Why did we choose

WHY

WHY

Enhancing Collaboration and Productivity

Information Overload

Advancements in NLP and Computer Vision

User Experience

3

Working process

HOW

work flow

NLP workflow

1.Input Text

2.Tokenization

3.Embedding

4.Transformer Model

5.Encoder (Text Understanding)

6.Attention Mechanism

7.Decoder (Text Generation)

8.Length Control( nucleus sampling)

Work flow

WorkFlow

1.Camera Setup:

2.Computer Vision Software

3.Object Detection

4.Perspective Transformation

5.Image Processing

6.Feature Extraction

7.Canvas Rendering:

8.Overlaying the Content.

Summary

Applications

applications

Applications

1.Content Curation:

2.Search Engines:

3.Document Summarization

4.E-Learning:

5.Business Meetings

6.Interactive Whiteboards

Challenges

Challenges

1.Content Quality:

2.Abstractive Summarization

3.Scalability

TIMELINE

TIMELINE

#1

#3

#2

Abstract

abstract

In this project we are develop a virtual board and text summarizer for smart

learning and teaching.virtual board is builded by using computer vision by

importing packages from mediapy(OpenCv).virtual board actually uses hand

tracking algorithm and develops the virtual board by importing aircanvas

technology for gesture recognition.

Text summarizer would take a audio as input and convert into a text or a

direct text and convert the text into summary points.NLP is used in text

summarization to analyse and understand the content of input text, score

sentences or phrases for importance, and generate coherent and concise

summaries. NLP techniques are used to preprocess the input text. This

involves tasks like tokenization and stemming of the input text.NLP algorithms

assess the importance of sentences or phrases within the text. Various

methods are used for this, including TF-IDF

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