Seasons Of Code
(Un)structured • Gagan Jain
WnCC - Seasons of Code
Seasons of Code is a programme launched by WnCC along the lines of the Google Summer of Code. It provides one with an opprtunity to learn and participate in a variety of interesting projects under the mentorship of the very best in our institute.
List of Running Projects
- Browser Based PDF manager
- Resume Script Generator
- Physicc : A Simple Physics Engine
- Image Colorization
- Language Model Based Syntax Autocompletion in a Text Editor
- Computer vision based web app
- Cribbit Cribbit (Open for PGs Only)
- Techster Texter
- Language Detection
- Book Tracker
- ResoBin - Not the bin we deserve but the bin we need!
- Moodify
- Agree to disagree
- Unscripted
- Watson (World's smartest assistant in your pocket)
- IITinder
- BriefKing
- Meta Learning - Learning to Learn
- Break free of the matrix, by building one!
- Procedurally Generated Infinite Open World
- Introduction to App Development
- PAC MAN
- Introduction to Web Development
- (Un)Clear
- Goal ICPC
- Traffic congestion modelling and rendering
- PyRated
- Tools for Data Science
- Machine Learning Based Metropolitan Air Pollution Estimation
- Audio controlled drone
- NLPlay with Transformers
- DIY FaceApp
- A Deep Dive into CNNs
- Competitive Coding
- Snake AI
- Facial Recognition App
- Gaming meets AI !!!
- R(ea)L Trader
- Computational Geometry
- Deep reinforcement learning - 2048 AI
- Reinforcement Learning to Finance
- Developing Hybrid ANN-Statistical Model for Robust Stock Market Prediction
- Si-Phy
- Astronomical Data-modelling and Interpretation
- Visual Perception for Self Driving Cars
- Convolutional Neural Networks and Applications
- Quantum Computing Algorithms
- Algorithm Visualizer
- Anime Club IITB Website using Django
- Machine Learning in Browser
(Un)structured
We all have used scanned copies of books and have been irritated by the fact that we cannot directly navigate to a particular section of the document or do a Ctrl+F. This project aims to take unstructured text as the input data and to give us nice and good looking structured text. This is not only restricted to properly written text as in a book but also extends to targeting problems like a self driving car detecting and understanding random road signs, automatic detection systems to record and interpret number plates of vehicles which did not follow the red light, and much more!
So, let’s dwell deep into the technicalities of the project. In simple words, the problem we’re targeting is as follows - ““You have an image as an input which contains text at certain locations in the image. You need to detect the text and generate the text present in the input as the output. This involves segmenting the given image to focus only on the part of the image which contains text. Then you need to implement text recognition to generate the output.””
So, having stated this, the project expects no prior knowledge of machine learning, but it will be a bonus if you know some stuff. You should be familiar with basics of linear algebra, and a little calculus(which you already know). Basic proficiency in Python is desirable but you can cover up even without that. As far as the proposal is concerned, you should be willing to dedicate time to the project because it is going to be learning intensive and time taking. Also mention your experience with coding in python and background in ML, if any. Also include what do you understand by the project description. You might require a lot of time to train your model just to check little changes.
Resources
Tentative Project Timeline
| Week Number | Tasks to be Completed |
|---|---|
| Week 1 | Learn the basics of version control (GitHub). Brush up your Python programming skills. |
| Week 2 | Install Ubuntu and setup a development environment. |
| Week 3 | Learn about Linux commands, working with numpy, Jupyter. Start learning about Neural Networks. |
| Week 4 | Continue learning about Deep Learning architectures. Start looking for good datasets to train and test the model. |
| Week 5 | Learn concepts of Image Classification and Recognition. Try simple implementations for the same. |
| Week 6-7 | Start working on the chosen dataset. Build a deep learning network which classifies and recognizes the data properly. |
| Week 8 | Work on tuning and training of the network on different datasets and analyse the results. |
| Week 9 | Buffer Week. Keep experimenting with the model to optimise results and improve accuracy. |
| Week 10 | Properly document the project. Write a short description about your project experience. |