{"id":17798170,"url":"https://github.com/maneprajakta/digit_recognition_web_app","last_synced_at":"2025-03-17T04:33:28.891Z","repository":{"id":178873857,"uuid":"273856772","full_name":"maneprajakta/Digit_Recognition_Web_App","owner":"maneprajakta","description":"A Hand Written Digit Recognition app trained on the MNIST dataset of Keras using the CNN model. skills used are Tensorflow, HTML,CSS,javascript.","archived":false,"fork":false,"pushed_at":"2020-06-21T17:10:29.000Z","size":1488,"stargazers_count":22,"open_issues_count":1,"forks_count":2,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-27T18:06:58.359Z","etag":null,"topics":["convolutional-neural-networks","digit-recognition","machile-learning","mnist-dataset","webapp"],"latest_commit_sha":null,"homepage":"https://maneprajakta.github.io/Digit_Recognition_Web_App/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/maneprajakta.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-21T07:33:30.000Z","updated_at":"2025-02-21T03:39:35.000Z","dependencies_parsed_at":null,"dependency_job_id":"eb5d823c-16d0-4ccd-a8b5-c0c62695e495","html_url":"https://github.com/maneprajakta/Digit_Recognition_Web_App","commit_stats":null,"previous_names":["maneprajakta/digit_recognition_web_app"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/maneprajakta%2FDigit_Recognition_Web_App","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/maneprajakta%2FDigit_Recognition_Web_App/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/maneprajakta%2FDigit_Recognition_Web_App/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/maneprajakta%2FDigit_Recognition_Web_App/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/maneprajakta","download_url":"https://codeload.github.com/maneprajakta/Digit_Recognition_Web_App/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243842135,"owners_count":20356610,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["convolutional-neural-networks","digit-recognition","machile-learning","mnist-dataset","webapp"],"created_at":"2024-10-27T11:59:29.308Z","updated_at":"2025-03-17T04:33:28.884Z","avatar_url":"https://github.com/maneprajakta.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Digit_Recognition_Web_App\n link : https://maneprajakta.github.io/Digit_Recognition_Web_App/\n\u003cbr\u003e\n\u003ch3\u003eStructure of App\u003c/h3\u003e\n\u003ch2\u003e keras - \u003e Tensorflow.js -\u003e(html + css + javascript)-\u003egithub pages\u003c/h1\u003e\n  \u003ch3\u003eHello World of Object Recognition!\u003c/h3\u003e\n \u003ch2\u003eAim:\u003c/h2\u003e To make a convolution neural network to recognise handwritten digits by training the model on MNIST dataset available in keras.\n \u003cbr\u003e\n \u003ch2\u003eMNIST DATASET:\u003c/h2\u003eThe training dataset contain 60000 images and testing contain 10000 images .Each image is 28x28 pixel and grey scale.\n  \u003cbr\u003e\n \u003ch2\u003eCNN MODEL OVERVIEW:\u003c/h2\u003e\n \u003cbr\u003e⚈ It is a 17 layer model with Conv2D,MaxPooling2D,BatchNormalization,Dense,Flatten and Dropout layer combination.\n \u003cbr\u003e⚈ Input layer has 32 neuron and output layer has 10 neurons as 10 different clases exsist.\n \u003cbr\u003e⚈ 30 epochs are used.\n \u003cbr\u003e⚈ Categorical_loss is loss function and adam is used for optimization.\n \u003cbr\u003e⚈ Model gives 99.15% accuracy.\n\u003ch2\u003eFor Deployment:\u003c/h2\u003eSave model using tensorflowjs converters as json file and weight as .h5 file.Use Tensorflow.js to load model and predict in javascript file\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaneprajakta%2Fdigit_recognition_web_app","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmaneprajakta%2Fdigit_recognition_web_app","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaneprajakta%2Fdigit_recognition_web_app/lists"}