OpenAI GPT Vision OCR API with Python: Extracting Information from Images
OpenAI GPT4 Vision OCR API Python
In this video we are going to teach you how to setup and extract information from images, using the OpenAI Vision API service. Later, we will show you the accuracy of the output, so please stick around.
OpenAI’s vision capabilities allow models like GPT-4o, GPT-4o mini, and GPT-4 Turbo to understand images. These models can take in images and answer questions about them. You can provide images either by passing a link or by encoding the image directly in the request. We will show you both methods later in the video.
For text extraction, OpenAI GPT-4o Vision uses a technology called Optical Character Recognition, or OCR for short. It analyzes images of text, deciphers the characters, and transforms them into editable digital text.
For image recognition and classification, OpenAI Vision uses LLM technology to interpret what it sees in the image you uploaded.
There are many ways that you can use this API in order to solve a myriad of problems involving images or documents.
For example - you are asking users to upload an image of a document for a specific purpose, such as proof of address or age. When the image is uploaded, you can ask OpenAI Vision what is displayed in the image, what text is included, or what type of document it is. The model will verify if the uploaded document is appropriate and contains the necessary information.
Other examples include extracting data from forms and tables in invoices or receipts, converting handwritten notes, and handling multiple languages in one image
📁 code repo on Github: https://github.com/TechExpertTutorials/OpenAIVision
Related Videos:
▶️ Python, Conda and VSCode Video: https://youtu.be/lGRwEcCHNtA
▶️ Azure OCR Video: https://youtu.be/67mudgk74hs
▶️ GCP OCR Video: https://youtu.be/hkKKfEqZvn4
▶️ OpenAI OCR Video: https://youtu.be/wlIFVfIYrPM
▶️ Gemini AI OCR Video: https://youtu.be/r2YGuPDECaE
▶️ AWS OCR Video: https://youtu.be/6h7fZ6brhsY
Related Videos/Playlists:
▶️ Google Cloud Vision API (Part 1): OCR Text Extraction Tutorial - https://youtu.be/q8QRd4CUuvs
▶️ Google Cloud Vision API (Part 2): Object Detection Tutorial - https://youtu.be/i2yFD8PsMvQ
▶️ Google Cloud Vision API (Part 3): Landmark Detection Tutorial - https://youtu.be/FZsdFvJLoa0
▶️ Google Cloud Vision API (Part 4): Facial Detection Tutorial - https://youtu.be/sZ4dP6JJhio
▶️ Google Cloud Vision API (Part 5): Label Detection Tutorial - https://youtu.be/s5doqd2VOds
▶️ Google Cloud Vision API Playlist - https://www.youtube.com/playlist?list=PLkTmsEazx3GVcEtCSLauTw4x4NgTSEGqM
💻 Our channel: https://youtube.com/@TechExpertTutorials
💥 link to subscribe: https://www.youtube.com/channel/UCniqO7kiYpJymnMfMFWS8XA?sub_confirmation=1
▶️ Most recent video: https://www.youtube.com/watch?v=G1jNf7P-2aw
OpenAI GPT4 Vision OCR API Python
In this video we are going to teach you how to setup and extract information from images, using the OpenAI Vision API service. Later, we will show you the accuracy of the output, so please stick around.
OpenAI’s vision capabilities allow models like GPT-4o, GPT-4o mini, and GPT-4 Turbo to understand images. These models can take in images and answer questions about them. You can provide images either by passing a link or by encoding the image directly in the request. We will show you both methods later in the video.
For text extraction, OpenAI GPT-4o Vision uses a technology called Optical Character Recognition, or OCR for short. It analyzes images of text, deciphers the characters, and transforms them into editable digital text.
For image recognition and classification, OpenAI Vision uses LLM technology to interpret what it sees in the image you uploaded.
There are many ways that you can use this API in order to solve a myriad of problems involving images or documents.
For example – you are asking users to upload an image of a document for a specific purpose, such as proof of address or age. When the image is uploaded, you can ask OpenAI Vision what is displayed in the image, what text is included, or what type of document it is. The model will verify if the uploaded document is appropriate and contains the necessary information.
Other examples include extracting data from forms and tables in invoices or receipts, converting handwritten notes, and handling multiple languages in one image
📁 code repo on Github: https://github.com/TechExpertTutorials/OpenAIVision
Related Videos:
▶️ Python, Conda and VSCode Video: https://youtu.be/lGRwEcCHNtA
▶️ Azure OCR Video: https://youtu.be/67mudgk74hs
▶️ GCP OCR Video: https://youtu.be/hkKKfEqZvn4
▶️ OpenAI OCR Video: https://youtu.be/wlIFVfIYrPM
▶️ Gemini AI OCR Video: https://youtu.be/r2YGuPDECaE
▶️ AWS OCR Video: https://youtu.be/6h7fZ6brhsY
Related Videos/Playlists:
▶️ Google Cloud Vision API (Part 1): OCR Text Extraction Tutorial – https://youtu.be/q8QRd4CUuvs
▶️ Google Cloud Vision API (Part 2): Object Detection Tutorial – https://youtu.be/i2yFD8PsMvQ
▶️ Google Cloud Vision API (Part 3): Landmark Detection Tutorial – https://youtu.be/FZsdFvJLoa0
▶️ Google Cloud Vision API (Part 4): Facial Detection Tutorial – https://youtu.be/sZ4dP6JJhio
▶️ Google Cloud Vision API (Part 5): Label Detection Tutorial – https://youtu.be/s5doqd2VOds
▶️ Google Cloud Vision API Playlist – https://www.youtube.com/playlist?list=PLkTmsEazx3GVcEtCSLauTw4x4NgTSEGqM
💻 Our channel: https://youtube.com/@TechExpertTutorials
💥 link to subscribe: https://www.youtube.com/channel/UCniqO7kiYpJymnMfMFWS8XA?sub_confirmation=1
▶️ Most recent video: https://www.youtube.com/watch?v=G1jNf7P-2aw
