The AI Handwritten Digits Recognition is the capability of computer applications to recognize the human handwritten digits. It is a hard task for the machine because handwritten digits are not perfect and can be made with many different shapes and sizes.
The handwritten digit recognition system is a way to tackle this problem which uses the image of a digit and recognizes the digit present in the image. Convolutional Neural Network model created using PyTorch library over the MNIST dataset to recognize handwritten digits .
Handwritten Digit Recognition is the capability of a computer to fete the mortal handwritten integers from different sources like images, papers, touch defenses, etc, and classify. them into 10 predefined classes (0-9). This has been a Content of bottomless- exploration in the field of deep literacy. Number recognition has numerous operations like number plate recognition, postal correspondence sorting, bank check processing, etc .
(2). In Handwritten number recognition, we face numerous challenges . because of different styles of jotting of different peoples as it . is not an Optic character recognition.
This exploration provides a comprehensive comparison between different machine literacy and deep literacy algorithms for the purpose of handwritten number recognition. For this, we've used Support .
Vector Machine, Multilayer Perceptron, and Convolutional . Neural Network. The comparison between these algorithms is carried out on the base of their delicacy, crimes, and .testing- training time corroborated by plots and maps that have been constructed using matplotlib for visualization.
Datasets Details : -
The MNIST dataset is an acronym that stands for the Modified National Institute of Standards and Technology dataset.
It is a dataset of 60,000 small square 28x28 pixel grayscale images of handwritten single digits between 0 and 9.
The task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively.
It is a widely used and deeply understood dataset and, for the most part, is "solved." Top-performing models are deep learning convolutional neural networks that achieve a classification accuracy of above 99%, with an error rate between 0.4 %and 0.2% on the hold out test dataset.
Handwritten character recognition is an extensive exploration area that formerly contains detailed ways of perpetration which include major literacy datasets, popular algorithms, . features scaling and point birth styles. MNIST dataset ( Modified National Institute of Norms and Technology database) is the subset of the NIST dataset which is a combination of two of NIST's databases Special. Database 1 and Special Database 3. Special Database 1 and Special Database 3 correspond of integers written by high academy scholars and workers of the United States Census Bureau,. independently. MNIST contains a aggregate of handwritten . number images (- training set and- test set) in . 28x28 pixel bounding box andanti-aliased. All these images have corresponding Y values which apprises what the number
Implementation Steps : -
- Import the libraries and load the dataset
- Preprocess the data
- Create the model
- Train the model
- Evaluate the model
- Create GUI to predict digits
Requirements .txt file :-
- torch
- numpy==1.16.5
- flask==1.1.1
- gunicorn
- matplotlib==3.3.1
- pillow==6.2.0
- flake8
- pip
- pylint
Technology Used in the project :-
We have developed this project using the below technology
- HTML : Page layout has been designed in HTML
- CSS : CSS has been used for all the desigining part
- JavaScript : All the validation task and animations has been developed by JavaScript
- Python : All the business logic has been implemented in Python
- Flask: Project has been developed over the Flask Framework
Supported Operating System :-
We can configure this project on following operating system.
- Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
- Python 3.6.10, PIP, Django.
- 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 : -
- python 3.6.8
- command 1 - python -m pip install --user -r requirements.txt
- command 2 - python app.py
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