Text Behind Image

Turn any image into usable text in seconds, fast and accurate.

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Inspiration

We were inspired to build Text Behind Image by the need for smarter ways to extract and understand content hidden within visual media. Often, important information is embedded in images, like diagrams, screenshots, or infographics, and manually analyzing them is slow and error-prone. We wanted to create a tool that leverages AI to make this process fast, accurate, and developer-friendly.

What it does

Text Behind Image allows users to input any image and instantly extract text or relevant information. The project can identify text layout, structure, and context, making it easy to integrate into workflows like content analysis, note-taking, or automated reporting. It transforms images into actionable data, saving time and reducing manual work.

How we built it

We built the project using Kiro as our core development assistant. Our process began with defining clear specs, user flows, and UI designs. Using Kiro, we generated structured TypeScript code, implemented real-time previews, and built interactive features. Mermaid diagrams helped visualize project architecture, and agent hooks automated repetitive tasks like updating project structure and refreshing UI previews.

The spec-driven approach ensured that every component aligned with our requirements from the start. By combining Kiro's code generation with iterative testing and refinement, we were able to produce a functional and polished application efficiently.

Challenges we ran into

One of the main challenges was translating high-level specifications into detailed, accurate code. Initially, some features did not work as expected due to the complexity of text extraction from images. We also had to ensure the UI was intuitive and responsive, which required careful iteration.

Additionally, coordinating prompts and hooks in Kiro to maintain synchronization between different parts of the project was a learning curve. Overcoming these challenges required careful planning, structured workflows, and repeated refinement.

Accomplishments that we're proud of

We are proud that Text Behind Image can extract text reliably from a variety of images while maintaining the correct structure and context. Kiro-generated TypeScript code worked on the first iteration in many cases, which accelerated development significantly. The mermaid diagrams and automated hooks also made the project workflow highly efficient and organized.

What we learned

This project taught us the value of spec-driven development and how AI assistants like Kiro can enhance productivity. We learned how to structure prompts effectively, create user flows, and automate workflows for consistent results. We also gained experience in merging AI-generated code with custom logic to build robust applications.

What's next for Text Behind Image

Next, we plan to expand support for more complex image types and integrate natural language understanding to extract not just text but context and meaning. We also aim to improve the UI and add collaboration features so teams can use the tool for research and reporting more effectively.

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