Recently created a custom GPT designed to extract and generate detailed text descriptions from images. Whether it’s a photograph, a chart, a table, or a complex visual representation, VisionScribe GPT converts it into structured, accessible, and SEO-friendly text.
This GPT is particularly useful for:
- Data Extraction & Processing – Extracts insights from charts, graphs, and infographics for better understanding and analysis.
- Web Indexing & SEO – Enhances image discoverability by providing search engines with rich textual descriptions.
- Accessibility – Helps visually impaired users by converting images into descriptive text.
Some examples on how it can be used:
Describing Images for Accessibility
A user uploads an image of a landscape, VisionScribe GPT generates:
The image captures a serene natural landscape featuring a calm river reflecting the sky and surrounding greenery. A tree leans slightly over the water, with its reflection forming a mirror-like effect on the surface. The sky is filled with scattered clouds, through which the sun’s rays peek, casting a warm, golden light over the scene. The grassy banks on either side of the river are lush and vibrant, with trees and shrubs adding depth to the composition. In the background, rolling fields and more trees stretch towards the horizon, enhancing the sense of tranquility and openness.
Alt text: A scenic river landscape with a tree leaning over the water, reflecting the sky filled with scattered clouds and sun rays. The grassy riverbank is lush with greenery, and distant fields and trees add depth.
Benefit: Enables visually impaired users to “see” the image through words, and helps create more accessible websites
Extracting Data from Charts & Graphs
A user uploads a graphic with charts, VisionScribe GPT generates:
Image Description:
The image is a line graph titled “U.S. Households By Total Yearly Income”, showing the percentage of U.S. households in three income categories from 1967 to 2007.
Key Elements of the Graph:
- X-Axis: Represents the years from 1967 to 2007.
- Y-Axis: Represents the percentage of households in each income category.
- Legend (Three Income Categories):
- Green Line: Households earning between $10k and $75k.
- Blue Line: Households earning above $75k.
- Red Line: Households earning below $10k.
Trends Observed:
- The percentage of households earning between $10k and $75k (green line) has gradually declined over time.
- The percentage of households earning above $75k (blue line) has increased steadily, especially after the early 1980s.
- The percentage of households earning below $10k (red line) has remained relatively stable with minor fluctuations.
Source:
The data is sourced from the Income, Poverty, and Health Insurance Coverage in the United States – 2007 (Table A-1).
Alt Text:
A line graph titled “U.S. Households By Total Yearly Income” from 1967 to 2007, showing three income categories. The percentage of households earning between $10k and $75k has decreased, while those earning above $75k have increased. The percentage of households earning below $10k has remained relatively stable. Data sourced from a 2007 report on income, poverty, and health insurance coverage.
Benefit: Saves time in manually extracting insights from complex visual data, creating accessible description for the visually impaired, and creating text description of graphics that can enhance SEO, searchability and indexing of the content.
How to access VisionScribe GPT?
🔹 Step 1: Visit the Custom GPT Page:
Click the following link to access: VisionScribe GPT
🔹 Step 2: Log in to ChatGPT
If you already have an OpenAI account, simply log in with your credentials. If you don’t have an account yet, sign up for free on the OpenAI platform.
🔹 Step 3: Start Chatting!
Once logged in, the easiest way to use it is just uploading an image, click enter and letting it do it’s magic!. Also, you could type your questions or upload relevant documents. VisionScribe GPT will process your request and provide insightful responses. You can refine your queries or ask for summaries, clarifications, and more.