Tag Archives: Convolutional neural network

Handwritten digit recognition Python Flask

Posted on by

Handwritten digit recognition is an important application of machine learning, particularly in the field of computer vision. The task involves identifying handwritten digits from an image and classifying them into the corresponding numerical values. In this project, we have developed a Flask-based application that recognizes handwritten digits using a pre-trained machine learning model.

Objective:

The objective of this project is to build a machine learning model that can accurately recognize handwritten digits and to develop a Flask-based web application that utilizes the model to recognize digits entered by users.

Methodology:

We used the MNIST dataset for training and testing our machine learning model. This dataset consists of 60,000 training images and 10,000 test images of handwritten digits from 0 to 9. We used a convolutional neural network (CNN) architecture to train our model on this dataset.

Once the model was trained and tested, we saved it as a serialized object using the joblib library. We then developed a Flask-based web application that allows users to draw a digit using their mouse or touchscreen and submit the image to the model for recognition.

Results:

Our machine learning model achieved an accuracy of 99.1% on the MNIST test set. When integrated with the Flask application, the model is able to accurately recognize handwritten digits drawn by users in real-time.

Technology Overview:

  1. Machine Learning – Machine learning is a subfield of artificial intelligence that enables machines to learn from data, without being explicitly programmed. In this project, we used machine learning algorithms to recognize handwritten digits.
  2. Convolutional Neural Networks – Convolutional Neural Networks (CNNs) are a class of deep neural networks commonly used in image processing and computer vision tasks. CNNs are designed to recognize visual patterns directly from pixel images, making them well-suited for tasks like image classification and object detection.
  3. Flask – Flask is a lightweight web framework that enables the development of web applications in Python. In this project, we used Flask to develop a web application that allows users to input handwritten digits and receive predictions from the trained machine learning model.
  4. MNIST Dataset – The MNIST dataset is a large database of handwritten digits commonly used for training and testing machine learning models. The dataset consists of 60,000 training images and 10,000 test images of handwritten digits from 0 to 9.
  5. Joblib – Joblib is a library for Python that enables the efficient serialization and deserialization of Python objects. In this project, we used Joblib to save and load the trained machine learning model.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.6.10, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : –

  1. python 3.6.8
  2. command 1 – python -m pip install –user -r requirements.txt
  3. command 2 – python app.py

Download Link

For Live Demo & Enquiry  :

WhatsApps : +916263056779

Email : official@projectworlds.in

Fire Detection Using Surveillence Camera web app Project with Source Code

Posted on by

Buy Now ₹1501

Introduction:

The objective of this project is to develop a web application that uses surveillance cameras to detect fire and alert users in real-time. The application uses computer vision algorithms and machine learning techniques to analyze video footage from the cameras and detect the presence of fire. The project aims to improve fire safety by detecting potential fire hazards early and allowing users to take appropriate action.

Methods:

The project involved several steps, including collecting and labeling a dataset of video footage that contained both fire and non-fire events, preprocessing the video footage to extract individual frames, and training a machine learning model using the preprocessed dataset. The machine learning model was a convolutional neural network (CNN) that was trained to detect the presence of fire in an image.

Once the machine learning model was trained, a web application was developed that allowed users to upload video footage from their surveillance cameras. The uploaded footage was analyzed frame by frame using the trained machine learning model to detect the presence of fire. If fire was detected, the application triggered an alert and notified the user via email or SMS. The application also provided a live video feed from the surveillance camera and highlighted the region where the fire was detected.

Results:

The developed web application was able to accurately detect the presence of fire in video footage from surveillance cameras. The machine learning model achieved an accuracy of over 95% on the test dataset, indicating that it was able to accurately distinguish between fire and non-fire events. The web application was also able to provide real-time alerts and notifications to users when fire was detected, allowing them to take appropriate action.

Discussion:

The developed web application has several potential applications in improving fire safety in buildings. For example, it can be used in warehouses, factories, and other industrial settings where fire hazards are common. The application can also be used in homes and other residential settings, alerting residents to potential fire hazards in real-time.

The project has several limitations that should be considered. One limitation is the need for high-quality video footage from surveillance cameras. The accuracy of the machine learning model is highly dependent on the quality of the video footage. Another limitation is the need for periodic retraining of the machine learning model to ensure that it continues to accurately detect fire over time.

Conclusion:

The project has demonstrated the feasibility of using surveillance cameras and machine learning algorithms to develop a web application for fire detection. The application has the potential to improve fire safety in various settings, including industrial and residential settings. Further research is needed to optimize the accuracy of the machine learning model and to develop additional features that can enhance the functionality of the application.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.6.10, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py