Extraction de texte et résumé avec le tutoriel EasyOCR et GPT-3

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

By the end of this AI tutorial, you will know how to use EasyOCR for text extraction from various sources like photos and harness the power of OpenAI's GPT-3 for text summarization!

Unraveling EasyOCR: A Software Powerhouse

EasyOCR, a private entity, stands out in the realm of software publishing, consultancy, and supply. They excel in creating ready-made software, operating systems software, business applications software, and computer games software for all platforms. Offering custom software solutions after a thorough analysis of user needs and problems enhances their market position.

YOLOv7 Unveiled: The Future of Object Detection

YOLOv7, the latest addition to the YOLO family of single-stage object detectors, is a game-changer in the field of object detection. This advanced model processes image frames through a backbone to extract features, which are then mixed and combined in a "neck" before passing to the "head" of the network. Here, it predicts the locations and classes of objects, identifying them with bounding boxes.

Significantly introduced by WongKinYiu and Alexey Bochkovskiy (AlexeyAB), YOLOv7 enhances bounding box accuracy and inference speed through several innovative changes to the YOLO network and training routines. Notable features include:

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

The YOLOv7 GitHub repository provides all the necessary code for training YOLOv7 on custom data, defined in PyTorch and written in Python.

Getting Started

Installing Dependencies

Start by downloading the necessary libraries required for EasyOCR and GPT-3.

Coding Environment

For this tutorial, I will use Visual Studio Code (VSC), but you can use any environment you prefer, including notebooks or Google Colab.

Note: It is practical to use a single file for this tutorial, though you may split the code into modules as needed.

Text Extraction from Images

For this task, we will utilize EasyOCR to create a class capable of extracting text from images.

Class Structure

__init__: Defines the Reader for English. It utilizes GPU if available and downloads the models to the ./models directory if not already present.
__call__: Allows direct calls to extract_text method in the instance, akin to a function.
extract_text: Accepts an image as an argument, returning a list of extracted texts and an image with bounding boxes, filtering out texts with confidence less than 45%.

We can now utilize this class to extract text from an image. To simplify this process, we will create a function for loading images.

Using an image from Adrian's previous tutorial, the results will reveal:

An image annotated with bounding boxes
Extracted text from the image

Not bad at all!

Text Summarization!

Now that we have successfully extracted text, we will proceed to summarization using GPT-3.

Setting Up GPT-3

We'll create a class to manage our requests to GPT:

Setup a .env file to store the OpenAI API key.
Define the class for GPT-3.
__init__: Sets the GPT-3 model and configuration, including the API key.
__call__: Similar to the previous class.
prediction: Facilitates making predictions based on a given prompt.
summarize: Summarizes the given text.

Testing the Application

After putting everything together, our code should look clear and structured. Running the code will yield:

An image with bounding boxes
The extracted text along with the summarization results

Wow! This allows us to create a simple application capable of summarizing text extracted from a regular photo. Enjoy leveraging this tool!

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