Abstract

This project introduces a powerful and scalable application designed for extracting text from images efficiently, utilizing the advanced capabilities of the GOT-OCR 2.0 model. The GOT-OCR 2.0 model, with its impressive 580 million parameters, is an integrated, fine-tuned, and end-to-end solution that combines a high-compression encoder with a long-context decoder. This state-of-the-art architecture ensures exceptional accuracy in recognizing text across a wide range of image formats.
The application supports multiple image types, automatically optimizes them for processing, and delivers highly accurate text recognition results. By incorporating cutting-edge Optical Character Recognition (OCR) technology within a user-friendly Gradio interface, this project streamlines the process of text extraction, making it accessible for developers and businesses alike.
A standout feature of this solution is its ability to extract text seamlessly from diverse image sources, enabling efficient workflows in areas such as document digitization, data processing, and content management. The combination of the GOT-OCR 2.0 model and the Gradio framework highlights the potential of integrating advanced machine learning models with intuitive application platforms to create innovative and user-friendly tools.
In summary, this project not only simplifies text extraction processes but also showcases the transformative potential of modern OCR technologies in solving real-world challenges. It is an excellent example of how advanced AI models can be leveraged to create practical and impactful solutions for text recognition and extraction.

Methodology

1. Tokenization

This step involves loading the necessary tools to interpret and process text data. The tokenizer breaks down the input text, while the GOT-OCR 2.0 model is prepared to perform text recognition using optimized settings. By the end of this step, the system is ready to handle images and extract text accurately.

2. Defining the Upload Folder

This step creates a folder called uploads to store the image files that users will upload for text extraction. If the folder already exists, the application will continue without any issues. This folder acts as a storage area for all input images before they are processed by the OCR system.

3. Implementing the OCR Processing and User Interface

This step integrates the OCR functionality into a user-friendly application. The system:
Processes uploaded images for text extraction.
Supports two OCR modes: plain text and formatted text.
Provides a clean interface where users can upload an image, select a mode, and view results.
The design ensures simplicity for users and efficient handling of images and outputs. Additionally, temporary files are managed to keep the application running smoothly.

4. Launching the OCR Application

This step makes the OCR app live and ready to use. By running the script, the application opens a user-friendly interface in a web browser. Users can upload images, choose the desired OCR mode, and get text extraction results instantly. It’s the final step that connects all previous functionalities and delivers a complete working application.

App Installation

Environment Setup

Creating an App

Launching an App

App Results

Optical Character Recognition App

Visit github for more details
Github reprository