{"id":19493941,"url":"https://github.com/geo-y20/standard-ocr-","last_synced_at":"2026-04-07T18:32:23.454Z","repository":{"id":214899776,"uuid":"737625847","full_name":"Geo-y20/Standard-OCR-","owner":"Geo-y20","description":"Explore the Standard OCR Project: a deep learning-based character recognition system leveraging advanced computer vision techniques. Detect characters in images using ResNet, Xception, Inception, and MobileNet models. 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The dataset used in this project can be found \u003ca href=\"https://www.kaggle.com/datasets/preatcher/standard-ocr-dataset\"\u003ehere\u003c/a\u003e. The dataset consists of two sections: Data and Data2, each having training and testing directories with 36 subdirectories representing different character classes. The training data contains 573 images per class, while the testing data includes approximately 88 images per class. Understanding the dataset's structure is crucial for proper organization and analysis.\u003c/p\u003e\n\n  \u003ch2\u003eProblem Statement\u003c/h2\u003e\n    \u003cp\u003eThe task is a computer vision challenge to detect characters in input images, emphasizing image processing, analysis, and modern deep learning techniques. It's more aligned with computer vision than Optical Character Recognition (OCR), utilizing various models like ResNet, Xception, Inception, and MobileNet to process and analyze the dataset for accurate predictions.\u003c/p\u003e\n\n  \u003ch2\u003eGitHub Repository\u003c/h2\u003e\n    \u003cp\u003eThe code for this project is hosted on GitHub. You can clone the repository using the following command:\u003c/p\u003e\n    \u003cpre\u003e\u003ccode\u003egh repo clone Geo-y20/Standard-OCR- \u003c/code\u003e\u003c/pre\u003e\n\n  \u003ch2\u003eSolution Framework\u003c/h2\u003e\n    \u003cul\u003e\n        \u003cli\u003e\u003cstrong\u003eSet Up\u003c/strong\u003e: Importing necessary modules, setting hyperparameters, and constants.\u003c/li\u003e\n        \u003cli\u003e\u003cstrong\u003eData Loading\u003c/strong\u003e: Loading the dataset into memory for processing.\u003c/li\u003e\n        \u003cli\u003e\u003cstrong\u003eData Processing\u003c/strong\u003e: Converting raw data, including techniques like data augmentation, normalization, and resizing images.\u003c/li\u003e\n        \u003cli\u003e\u003cstrong\u003eData Visualization\u003c/strong\u003e: Inspecting the dataset for insights and potential issues.\u003c/li\u003e\n        \u003cli\u003e\u003cstrong\u003eBackbone Comparison\u003c/strong\u003e: Comparing different pre-trained backbones to identify the best performer.\u003c/li\u003e\n        \u003cli\u003e\u003cstrong\u003eModel Building\u003c/strong\u003e: Constructing a model architecture using selected backbones.\u003c/li\u003e\n        \u003cli\u003e\u003cstrong\u003eModel Predictions\u003c/strong\u003e: Evaluating model performance on unseen data, analyzing predictions, and identifying areas for improvement.\u003c/li\u003e\n    \u003c/ul\u003e\n\n  \u003ch2\u003eFolder Structure\u003c/h2\u003e\n    \u003cul\u003e\n        \u003cli\u003e\u003ccode\u003eapp.py\u003c/code\u003e: Contains Flask web application code for image prediction.\u003c/li\u003e\n        \u003cli\u003e\u003ccode\u003etemplates/\u003c/code\u003e: Directory for HTML templates.\u003c/li\u003e\n        \u003cli\u003e\u003ccode\u003estatic/\u003c/code\u003e: Directory for static files (CSS, JS, images).\u003c/li\u003e\n    \u003c/ul\u003e\n\n  \u003ch2\u003eGetting Started\u003c/h2\u003e\n    \u003col\u003e\n        \u003cli\u003eInstall necessary libraries and dependencies.\u003c/li\u003e\n        \u003cli\u003eEnsure Python environment compatibility.\u003c/li\u003e\n        \u003cli\u003eRun \u003ccode\u003epip install -r requirements.txt\u003c/code\u003e to install dependencies.\u003c/li\u003e\n        \u003cli\u003eTrain and save your model using the provided dataset.\u003c/li\u003e\n        \u003cli\u003eUpdate \u003ccode\u003eapp.py\u003c/code\u003e with the path to your trained model.\u003c/li\u003e\n        \u003cli\u003eRun the Flask application (\u003ccode\u003epython app.py\u003c/code\u003e) and navigate to \u003ccode\u003elocalhost:5000\u003c/code\u003e in your browser.\u003c/li\u003e\n    \u003c/ol\u003e\n\n  \u003ch2\u003eUsage\u003c/h2\u003e\n    \u003col\u003e\n        \u003cli\u003eAccess the application through the browser.\u003c/li\u003e\n        \u003cli\u003eUpload an image containing characters.\u003c/li\u003e\n        \u003cli\u003eGet predictions for the characters present in the image.\u003c/li\u003e\n    \u003c/ol\u003e\n\n  \u003ch2\u003eDownload the Model\u003c/h2\u003e\n    \u003cp\u003eTo download the H5 model file, click \u003ca href=\"https://drive.google.com/file/d/1RlZ0oqee9UgcBOi5w07dTNKwsJ8nOaD1/view?usp=sharing\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\n  \u003ch2\u003eSample Images\u003c/h2\u003e\n    \u003cp\u003e\n      \u003cimg src=\"https://drive.google.com/uc?export=view\u0026id=1MzsL6GlmHYlLxPBp8vJ1VMuzp_URVfQF\" alt=\"Sample 1\"\u003e\n      \u003cimg src=\"https://drive.google.com/uc?export=view\u0026id=1APZ0ElDgxUzDTL36ovjwbZmSL3fWxUVt\" alt=\"Sample 2\"\u003e\n    \u003c/p\u003e\n\u003c/body\u003e\n\u003c/html\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeo-y20%2Fstandard-ocr-","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgeo-y20%2Fstandard-ocr-","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeo-y20%2Fstandard-ocr-/lists"}