Text Extraction and Summarization with EasyOCR and GPT-3 Tutorial

Acquiring Advanced Skills: YOLOv7 and GPT-3 at Your Fingertips

By the end of this AI tutorial, you’ll learn how to leverage EasyOCR for text extraction from various sources, including images, and harness the robust capabilities of OpenAI's GPT-3 for effective text summarization!

Unraveling EasyOCR: A Software Powerhouse

EasyOCR is a powerful tool in the realm of text detection and extraction. This private entity excels in software publishing, consultancy, and supply. Specializing in developing ready-made software solutions, EasyOCR caters to various platforms, with a focus on creating business applications and operating systems. They further enhance their offerings by delivering customized software tailored to user needs and requirements.

YOLOv7 Unveiled: The Future of Object Detection

YOLOv7 stands out as the latest innovation in the YOLO (You Only Look Once) family of single-stage object detectors. This groundbreaking model provides improved accuracy and speed in the image detection space. Utilizing a sophisticated processing pipeline, YOLOv7 extracts features through a backbone, transmits them through a 'neck', and finally predicts, through the 'head' of the network, the locations and classifications of objects within the given frames.

Developed by WongKinYiu and Alexey Bochkovskiy, YOLOv7 enhances the existing YOLO framework through fundamental changes in the network architecture and training routines. This model incorporates advanced techniques such as:

• Extended efficient layer aggregation
• Model scaling techniques
• Re-parameterization planning
• An auxiliary head for coarse-to-fine predictions

The YOLOv7 GitHub repository is a valuable resource, providing the necessary code to initiate training on custom datasets powered by PyTorch and implemented in Python.

Getting Started

To kick things off, we will need to install some essential dependencies.

Installing Dependencies

Begin by installing the required libraries necessary for both YOLOv7 and EasyOCR to function seamlessly.

Coding

For this tutorial, I will utilize Visual Studio Code (VSC), but you’re free to use any development environment, including Jupyter notebooks or Google Colab.

Setting up Dependencies

Import all necessary dependencies for your project to function effectively.

Text Extraction from Image

To perform text extraction, we will employ EasyOCR. Below is the structure of our EasyOCR class:

class EasyOCR:
    def __init__(self):
        self.reader = easyocr.Reader(['en'], gpu=True)
    
    def extract_text(self, image):
        results = self.reader.readtext(image)
        return results

The class initializes the OCR reader for English, uses GPU if available, and ensures necessary models are downloaded. Next, we extract text from an image, filtering results to include only those with a confidence level above 45%.

Text Summarization with GPT-3

Having extracted text, we can now move on to summarizing it using OpenAI’s GPT-3. Here’s a foundational structure for the GPT-3 summarization class:

class GPT3:
    def __init__(self, api_key):
        self.api_key = api_key
    
    def summarize(self, text):
        response = openai.Completion.create(
            model='text-davinci-003',
            prompt=text,
            max_tokens=50
        )
        return response.choices[0].text.strip()

This structure sets up the API key and model to be used, allowing the class to produce summaries based on a given text prompt.

Testing the Application

Now that we have our code set up, we can run it to test both text extraction and summarization. Here’s what the output looks like:

print("Extracted Text:", extracted_text)
print("Summary:", summary)

In doing so, we validate that we can seamlessly create an application that summarizes text extracted from images—how riveting!

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