Handwritten Digit Recognition using ML Project
Develop this important project with the help of expert guidance!
Introduction to Project:
Handwritten digit recognition is the process to provide the ability to machines to recognize human handwritten digits. It is not an easy task for the machine because handwritten digits are not perfect, vary from person-to-person, and can be made with many different flavors. Among thousands of datasets available in the market, MNIST is the most popular dataset for enthusiasts of machine learning and deep learning. Above 60,000 plus training images of handwritten digits from zero to nine and more than 10,000 images for testing are present in the MNIST dataset. So, 10 different classes are in the MNIST dataset. The images of handwritten digits are shown as a matrix of 28×28 where every cell consists of a grayscale pixel value.
1. Deep Learning
2. Neural Networks
3. Tensorflow & Keras Libraries,Tkinter, Anaconda, Jupyter
Process followed for the project
1. Import libraries and dataset
2. The Data Preprocessing
3. Create the model
4. Train the model
5. Evaluate the model
6. Create GUI to predict digits
Prerequisites, who should this project?
Basic knowledge of deep learning with Keras library, the Tkinter library for GUI building, and Python programming are required to run this amazing project.
What you will receive?
1. Project Tutorials
2. Online Course Material Certificate
3. One-on-One Expert Assistance.
What you will build?
1. Working Handwritten Digit Recognition Using ML
3. Project Development Skills
4. Portfolio Tkinter GUI App
Why build this project?
1. Machine learning and deep learning plays an important role in computer technology and artificial intelligence.
2. In this project course, you will learn how to use ML to recognize handwritten digits and build GUI App using Tkinter
3. This ML model finds its application in recognizing number plates of vehicles, processing bank cheque amounts, numeric entries in forms filled up by hand etc.
Mentor for this project:
Handwritten digit recognition is the classification ability of a computer to detect human handwritten digits from various sources such as photographs, papers, touch screens and classify them among one of the digits from 0-9.
The applications of digit recognition include in postal mail sorting, bank check processing, data entry, etc. Handwritten digit recognition not only has professional and commercial applications but also practical applications in our daily life. It can be of great help to the visually impaired to make the lives easier.
Most standard implementations of neural networks achieve an accuracy of (98–99) percent in correctly classifying the handwritten digits.
MNIST is a widely used dataset of handwritten digits that contains 60,000 handwritten digits for training a machine learning model and 10,000 handwritten digits for testing the model. It was introduced in 1998 and has become a standard benchmark for classification tasks.
The main difficulty in the handwritten digits recognition is different handwritten style which is a very personal behavior where there are a lot of models for numbers based on the angles, length of the segments, stress on some parts of numbers, etc.
The task of handwritten digit recognition, using a classifier, has great importance and use such as – online handwriting recognition on computer tablets, recognize zip codes on mail for postal mail sorting, processing bank check amounts, numeric entries in forms filled up by hand (for example - tax forms) and so on.
Anybody who has prior knowledge on Python and want to develop machine learning skills can take up this project-based course.
Our expert trainers are available to clear your doubts. It is very simple. You will just have to select a time slot that is suitable for you and our trainer will call you at that time and help you. The doubt clarification sessions are always 1-1. So you can ask as many doubts as you want any number of times!
Please feel free to contact us in case of any queries. Email: email@example.com. We prefer email contact as that can be replied by the right expert who can answer your question!
The fee you pay includes:
1. Course content
2. Charges for technical support
Ofcourse, and we strongly encuorage that. The course is fun and learning is more effective when you work together, learn together and build together!
A team can have maximum of 4 members. All team members will get separate logins and certificate. Course Fees will be varying in this case.
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