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Doodle Recognition — 5-Class CNN
An interactive computer vision project that classifies hand-drawn sketches into five categories using a Convolutional Neural Network (CNN) trained on Google's Quick, Draw! dataset.
Overview
The project explores how deep learning can recognize simple drawings from their visual patterns. A CNN learns features such as edges, curves, and shapes to distinguish between five classes:
Apple
Cookie
Dog
Eye
Face
The goal is to connect model training with an interactive drawing experience, allowing users to submit sketches and receive a predicted class.
Technical Approach
Data preparation: Process the drawing data into model-ready arrays and corresponding class labels.
Model development: Train a CNN using TensorFlow/Keras for five-class image classification.
Inference: Preprocess user drawings consistently with the training data before passing them to the trained model.
User interface: Provide a drawing canvas and display the model's predicted class.
Tech Stack
Python
TensorFlow / Keras
NumPy
Google Quick, Draw! dataset
What This Project Demonstrates
CNN-based image classification
Image preprocessing and labelled dataset preparation
Multi-class prediction
Integration of a trained model into an interactive application
Separation of model training, inference, and user-interface components
Source Code
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Overview
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The Problem:
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My Solution:
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The platform includes:
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Crowd occupancy estimation.
Crowd-density classification.
Entry and exit line counting.
Zone-based monitoring.
Incident and anomaly monitoring workflows.
Camera health and tamper monitoring.
Evidence management and reporting.
Telegram alert notifications.
Remote edge camera processing.
Live monitoring dashboard.
Technical Implementation:
Machine Learning & Computer Vision
Python
YOLO / Ultralytics
OpenCV
NumPy
Pandas
Backend:
Python
FastAPI
REST API
Frontend:
React
Vite
JavaScript
Other Technologies
Git
GitHub
Telegram Bot API
JSON/CSV data storage
My Role
Founder & Full-Stack Machine Learning Developer
For entry/exit line counting, I'd test someone pausing on the boundary and stepping back without entering. Do you require a track to move fully between two zones before recording an entry, so small position changes don't become extra visits?