Synopsis

On

REAL-TIME CHAT WEB APP WITH TRANSLATION FEATURE

(TranslaTalk)

Submitted in partial fulfillment of the requirement

For the award of the degree of
B.TECH (CSE)
In
Computer Science & Engineering
Submitted By

AMIT SAINI (2200680100053)

ANKIT KUMAR (2200680100058)
DEEPANSHU TYAGI (2200680100117)
DEV ARYA (2200680100118)

Under the guidance

Dr. Vikas Shrivastava

Department of Computer Science & Engineering

Meerut Institute of Engineering & Technology, Meerut
 TABLE OF CONTENTS

TOPIC PAGE No.

1. INTRODUCTION 3

2. LITERATURE SURVEY 4

3. IDENTIFICATION OF RESEARCH PROBLEM/OBJECTIVES 6

4. EXPECTED IMPACT ON ACADEMICS /INDUSTRY 7

5. RESEARCH METHODOLOGY 9

6. REFERENCES 12
1. INTRODUCTION

In today’s digitally connected world, real-time communication is essential across all fields —
whether it be education, business, healthcare, or social networking. However, language diversity
often creates a significant barrier in seamless interaction between individuals from different
linguistic backgrounds. Traditional chat applications allow users to exchange messages, but most
do not offer built-in translation features, making cross-language communication difficult and
ineffective.

To address this challenge, we propose TranslaTalk, a real-time multilingual chat web application
that enables users to send and receive messages in different languages with automatic translation.
The application provides an intuitive and user-friendly interface with modern features such as real-
time messaging, emoji reactions, and plans for voice and video message translation in future
versions.

TranslaTalk aims to bridge the language gap and provide a universal communication platform
where users can converse in their native languages without worrying about being misunderstood.
The solution is designed to be scalable, accessible via web browsers, and easy to use for people
from all backgrounds.

Aim and Objectives of Research

The primary aim of this project is to develop a real-time multilingual chat web application,
named TranslaTalk, that enables users to communicate across different languages seamlessly by
providing automatic translation of messages within the chat interface.

1. To develop a user-friendly, real-time multilingual chat web application.

2. To integrate automatic message translation using APIs like Google Translate.
3. To enable smooth bilingual communication between users.
4. To implement secure user login and message storage using MongoDB.
5. To support basic chat features like emojis, timestamps, and chat history.
6. To build a scalable system ready for future voice/video translation integration.
2. Literature Review
[1] In the rapidly evolving field of real-time communication, several platforms and services have
emerged aiming to connect users across geographic and linguistic boundaries. However, most
existing solutions either provide limited functionality or lack integration between key components
like messaging, translation, and user experience. A critical analysis of various available tools
reveals both their strengths and significant limitations.

[2] Google Translate, for instance, is one of the most commonly used language translation
services globally. While it offers high translation accuracy and supports a wide range of
languages, it is primarily designed as a standalone tool. In chat-based communication, it
requires users to copy text from a conversation, paste it into the Google Translate interface,
obtain the translation, and then copy it back into the chat window — making the overall
experience fragmented and inefficient for real-time interaction.

[3] Microsoft Translator Conversations, another known platform, supports real-time

multilingual group conversations. However, its interface is relatively formal and lacks the
natural feel of casual chatting platforms. Features such as emoji support, media sharing,
modern UI elements, and customizable chat environments are missing, which reduces user
engagement and accessibility, especially for non-corporate or casual use cases.

[4] Additionally, platforms like Telegram have integrated translation through third-party bots.
Although these bots are functional and can translate messages in real-time within the app, they
are not intuitive for common users. The need to use commands and interact with bots adds
complexity and disrupts the smooth, human-like experience expected from a chat platform.
Furthermore, messages translated through bots often do not maintain the context or tone,
resulting in communication errors or misunderstandings. Sentence with every other sentence to
create a similarity matrix.
Calculate the importance score for each sentence based on its semantic similarity to other
sentences.

[5] Apps like Speak & Translate focus on real-time voice translation and are mainly designed for
mobile use. While they serve travelers or short-term communicators well, they lack text-based
communication, history saving, and are not suitable for continuous conversations or integration
into web-based environments. Their absence of features like chat history, user authentication, and
cross-platform usage makes them unsuitable for scalable or professional use or concepts as nodes
and their relationships as edges in a graph. The number of edges and their types can then be used to
compute a similarity score. . But the time complexity is very high to extract the summary.

[6] From a research perspective, several papers and academic contributions have focused on
Natural Language Processing (NLP), Machine Translation (MT), and Language Modelling.
These studies have proposed models for semantic translation, contextual understanding, and multi-
lingual AI frameworks. However, most of these remain theoretical, simulation-based, or are used in
limited experimental environments. Very few projects have focused on converting such language
models into practical real-time communication tools that the average user can access and
operate.

[7] This overall analysis highlights a major research and implementation gap — there is a clear
lack of an all-in-one, real-time, user-friendly multilingual chat application that supports
natural communication, smart translation, and rich features like emojis, themes, and secure login
systems. TranslaTalk is designed to fill these gaps by integrating the best of translation APIs with
real-time chat features, providing a seamless and scalable experience for global users. It bridges the
gap between academic NLP models and everyday communication needs, offering a platform where
users can connect, understand, and express — regardless of language.
3. Identification of Research Problem
In the present digital age, communication across borders has become essential — be it for
education, global teamwork, business, or social interaction. While the internet has brought people
closer, language remains one of the most significant barriers in real-time communication.
People across different regions still find it difficult to chat and collaborate due to the lack of a
common language.

Several tools such as Google Translate or Microsoft Translator exist for converting text from one
language to another. However, these are standalone translation services and do not offer seamless
integration into live chat platforms. Users are required to switch between applications, copy and
paste text, and manually manage conversations — which breaks the flow and makes real-time
multilingual communication frustrating and inefficient.

Moreover, popular chatting apps like WhatsApp, Telegram, or Facebook Messenger do not
natively support automatic real-time translation. Third-party bots or extensions exist, but they
are either too complex for everyday users or fail to retain the tone, emotion, and context of
messages — resulting in miscommunication or loss of meaning.

In addition, current systems do not address the need for a complete communication experience
that includes emojis, message history, user accounts, media sharing, and theme customization —
all while supporting multilingual conversations.

This clear lack of an integrated, user-friendly, real-time translation-based chat application

represents a major research and implementation gap. People want to communicate freely in their
native language and still be understood globally — without needing to learn new languages or rely
on complex manual processes.

Therefore, there is a strong need to develop a system like TranslaTalk that fills this void and
allows seamless, accurate, and real-time multilingual chat communication with modern
features and future scope for voice and video-based translation.
4. Expected Impact on Academics /Industry

Impact on Academia:

1. Research Advancement in NLP and Translation:

TranslaTalk offers a practical implementation of natural language processing (NLP) and
real-time translation models. It helps bridge the gap between theoretical models taught in
academics and real-world application.
2. Learning for Future Developers:
This project serves as a learning framework for students and educators to understand full-
stack development, real-time communication systems, and integration of third-party APIs.
3. Innovation in Capstone Projects:
Academic institutions can use this as a reference or starting point for future research on AI-
driven multilingual communication, contextual translation, and cross-cultural user
experience design
4. Project-Based Learning Inspiration:
It encourages students to go beyond basic CRUD apps and build real-world systems with
global impact using open-source tools, APIs, and creative thinking.

Impact on Industry:

1. Global Business Communication:

Industries with international teams and customers (like BPOs, IT services, support centers)
can use such tools to break language barriers and improve productivity without hiring
multilingual staff.
2. Customer Support & Chatbots:
Companies can integrate TranslaTalk's concept into automated support systems to handle
multilingual customer queries in real-time, improving service quality and user satisfaction.
3. Remote Teams & Freelancers:
In the era of remote work, this system allows professionals from different linguistic
backgrounds to collaborate without any communication delay or misunderstanding.
4. Scalable for Product Integration:
The concept can be integrated into existing chat systems, ERPs, or SaaS platforms —
making it a high-value add-on for tech companies, especially startups.
5. Research Methodology
The research methodology adopted for the development of TranslaTalk – A Real-Time
Multilingual Chat Web Application is a combination of applied research and experimental
development, focusing on implementing practical solutions to real-world problems. The entire
methodology is structured in well-defined phases to ensure effective planning, development, and
evaluation.
1. Problem Identification and Requirement Analysis
The first step involved identifying the communication gap due to language barriers in real-time
chat environments. A detailed study of existing tools such as Google Translate, Telegram Bots, and
Microsoft Translator was conducted. Based on the shortcomings found in these platforms, the
requirements for an integrated multilingual chat system were derived.

Key activities:

 Literature review on existing solutions and APIs

 User behavior observation and feedback collection
 Defining the key functional and non-functional requirements

2. Technology Selection

A suitable tech stack was chosen based on project scope, scalability, and future enhancements. The
following were finalized:

 Frontend: HTML, CSS, JavaScript

 Backend: Python with Django Framework
 Database: MongoDB with MongoEngine ODM
 Translation: Google Translate API (or alternatives)
 Communication: AJAX / Django Channels (optional)
 Authentication: Django’s built-in auth system

3. System Design

A modular, layered architecture was designed to maintain separation of concerns and enable
scalability.

 UI/UX Design: Wireframes and responsive layouts for chat interface

  Architecture Design: Flow diagram showing how messages move from sender → backend
→ translation → receiver
 Database Design: User models, message models, language preferences

Tools used:

 Draw.io for architecture diagrams

 Figma for UI mockups (optional)
 ER diagrams for MongoDB schema planning

4. Development Phase
Agile methodology was used in short sprints to implement and test each module.

 User authentication module

 Chat input and message handling
 Language detection & real-time translation integration
 Emoji support and frontend enhancements
 MongoDB connection and chat history storage
 Basic security implementations (input validation, CSRF protection)

5. Testing and Debugging

Each component was tested using unit tests, manual testing, and browser-based real-time
communication testing.

 Cross-browser and cross-language testing

 Accuracy of translated messages
 Load testing for multiple users (basic simulation)
 UI/UX feedback from users and mentors

6. Evaluation & Improvements

 Feedback from peers, faculty, and test users was collected
 UI and response time were optimized
 Future modules (voice translation, video chat) planned for next phases
 Documentation and code commenting were done for better maintainability

7. Final Deployment (Optional in Project Phase)

If required, the system can be deployed on a cloud server (e.g., Heroku or Render) with secure
endpoints and database hosting via MongoDB Atlas.

Methodology Outcome:
The proposed methodology helped in building a scalable, real-time, multilingual chat system
which is:

 Easy to use
 Accurate in translation
 Ready for academic evaluation and real-world deployment
5. References

1. Google Cloud. (n.d.). Cloud Translation API Documentation. Retrieved from

https://cloud.google.com/translate/docs

2. Microsoft. (n.d.). Microsoft Translator for Conversations. Retrieved from

https://translator.microsoft.com/

3. MongoDB Inc. (n.d.). MongoEngine: Document-Object Mapper for working with

MongoDB from Python. Retrieved from

https://docs.mongoengine.org/

4. Hossain, M. S., & Muhammad, G. (2020). Cloud-assisted speech translation services

for global communication: A case study. Journal of Network and Computer Applications.

https://doi.org/10.1016/j.jnca.2020.102759

5. Kumar, A., & Sharma, R. (2021). Real-Time Language Translation Chat Application
Using Google API. International Journal of Computer Applications.

https://www.ijcaonline.org/

6. Medium Blogs. (n.d.). Building a Chat Application with Real-Time Translation Using
Python. Retrieved from.

https://medium.com/