Secret ChatGPT trick to read images inside of PDFs

Описание: Ever wondered if ChatGPT can go beyond text and delve into images within PDFs?

Contrary to popular belief, it can!

Join Jordan Wilson, the host of Everyday AI, as he reveals this groundbreaking technique, showing you how ChatGPT can work with images embedded in your PDFs.

#ai #generativeai #genai #chatgpt #chatgpt4 #aitools #computervision #openai #openaichatgpt #chatgpttricks #chatgpthack #PDFAnalysis

CHAPTERS:
00:00 - Can ChatGPT analyze images within a PDF
01:05 - How to get ChatGPT to analyze images in a PDF

Prompt for Handling Images in PDFs with Computer Vision and Python (Advanced Data Analysis)
Objective:You, ChatGPT, will analyze and extract contents from images embedded in a PDF file. The images may contain screenshots, visual content, or graphics. Use advanced computer vision (CV) techniques and Python tools to accurately extract text and data from the images. This prompt instructs you to go beyond the standard PDF OCR modes and fully utilize computer vision and Python capabilities.
Instructions for ChatGPT:Do not use standard OCR modes that only scan the text layer of a PDF. Instead, use a combination of computer vision techniques and Python to analyze each image extracted from the PDF.
Execution Process:
PDF Parsing and Page Processing:Open the PDF using PyMuPDF (or an equivalent library such as PDFPlumber) to analyze each page and identify embedded images. Ensure all images are detected and parsed, even if they are embedded within the PDF as graphical elements.
Image Metadata Extraction:Extract image metadata, including image resolution, width, height, bit depth (BPC), and color space (e.g., RGB). Record this metadata for each image before analyzing the content.
Image Extraction and Format Handling:Extract each image from the PDF and convert it to a usable format (e.g., PNG, JPEG) for analysis. Ensure the images are saved correctly for further processing. Handle different image formats (JPEG, PNG) without distortion or loss of quality.
Advanced OCR (Optical Character Recognition):
• Use OCR on Extracted Images:For each image, apply Tesseract or an equivalent OCR engine to extract any text contained in the image. Pre-process images (e.g., resizing, binarizing, denoising) to improve OCR accuracy. Use thresholding techniques if necessary to enhance clarity.
• Configure OCR for Multi-Language Support:Ensure that OCR is configured for English (lang='eng'), or adapt it based on the language detected in the images.
Computer Vision Techniques:
• Text Block Segmentation:Use segmentation techniques to divide the image into logical sections. Apply CV filters or edge detection to locate headers, paragraphs, and visual elements.
• Object Detection:If an image contains objects such as logos, charts, or tables, apply object detection techniques to extract meaningful data. Use contour analysis or similar methods to identify non-text elements.
Data Structuring:Present the extracted text in a structured format. Include page numbers, image index, and separate sections of text if multiple blocks are detected within an image. For images with complex graphics or little text, provide a description of the visual content (e.g., "graph showing AI adoption trends").
Handling Graphical Elements:
• If images contain graphical elements or are primarily non-text-based (e.g., screenshots with charts or figures), identify these as "graphical" sections.
• Attempt to extract relevant data from charts, diagrams, or tables using image processing and edge detection techniques.
• If the content cannot be read via OCR, describe the visual information.
Error Handling:If an image is of low resolution or contains unreadable text, log the issue and skip to the next image without interrupting the overall process.
Expected Output:
For each page of the PDF, you will:
1 Provide the image metadata (resolution, format, bit depth, etc.).
2 Extract and return the text contained within the image (via OCR), formatted by page and section.
3 Identify and describe any non-text graphical content (e.g., tables, charts).
Additional Notes:
• Ensure that all images extracted from the PDF are preserved in their original form, allowing for post-processing or re-analysis if needed.
• If text extraction is not possible for certain images, provide a detailed description of the visual content.
• Please specifically focus on Page 2 of this PDF. Please tell me every detail you can of that image embedded.
• Using advanced data analysis/Python/computer vision, please analyze the contents of the images embedded in the PDF and explain what the images contain.
• I do not care about the metadata of the image embedded on Page 1 and page 2. I want to know, very specifically, what the visuals say from the image(s) embedded in the PDF on page 1 and page 2.
Please be exhaustive in telling me what that visual contains. If there's text in the image, please transcribe it all. If there are other visual elements within the image, ple ....