Image Digitalization is a project aimed at transforming handwritten notes into tidy texts using artificial intelligence. The project utilizes various technologies including Python, Java, HTML, and CSS to achieve its goal.
The project is organized into different components:
The Python backend, built using the Flask framework, serves as the core of the project. It utilizes several libraries and APIs to handle image processing tasks and backend functionalities. Key libraries used include: rake_nltk: Utilized for keyword extraction from the uploaded images. RAKE (Rapid Automatic Keyword Extraction) is a natural language processing algorithm used to identify and extract keywords from text. googlesearch: Employed to generate search results based on the extracted keywords. This library enables the application to perform Google searches and retrieve relevant links. google.cloud.vision_v1: Integrated to extract text from the uploaded images using Optical Character Recognition (OCR) technology. This library allows the application to analyze images and extract text content accurately. This addition provides clarity on the specific functionalities of each library and how they contribute to the image digitalization process in the Python backend.
Java functions are utilized for specific tasks that require complex processing or integration with external libraries. For example, Java functions might be used for advanced image manipulation or machine learning tasks.
HTML templates are used to create the user interface (UI) for the web application. They define the layout and structure of the different pages where users interact with the application. Templates are rendered dynamically based on user actions and backend responses.
CSS stylesheets are employed to style the HTML elements and enhance the visual appeal of the UI. They define the colors, fonts, layout, and other visual aspects of the web pages.
To run the Image Digitalization project locally, follow these steps:
Clone the GitHub repository to your local machine. Install the necessary dependencies for the Python backend using pip. Run the Flask application to start the backend server. Open the web application in your browser to access the UI. Upload an image containing handwritten notes to initiate the digitalization process. Explore the generated text, keywords, search results, and other outputs provided by the application. Conclusion: The Image Digitalization project demonstrates the power of artificial intelligence and web technologies in converting handwritten content into digital format. By leveraging Python for backend processing, Java for advanced tasks, and HTML/CSS for frontend presentation, the project offers a seamless user experience and valuable functionality for researchers, students, and anyone in need of digitizing handwritten notes. Further enhancements and integrations can be made to expand the capabilities and usability of the application.