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UI Screenshot to Structured JSON

· Last updated：2026-11-01

Turn a screenshot into an editable visual layout tree, component inventory and coding handoff.

Details

What this tool helps you do

Upload a static screenshot and consent to third-party AI processing. Uses only deepseek/deepseek-v4.1-flash-1017 via OpenRouter, with no model substitution or automatic retry. Quick mode uses one request; precise mode adds at most two crop reviews. Crops are mapped back to original coordinates; invalid patches are discarded and marked partial. Inspect component overlays, correct text, type, parents and boxes, and attach change requests. JSON and Markdown downloads include local corrections, usage and review status, not the image itself. Visual structure and styles are approximations, not original DOM/CSS; hidden behavior is unknown. Model confidence is not measured accuracy. Masking replaces pixels before upload. Offline demo is synthetic and makes no AI calls. Provider retention policies apply; no guarantee of zero retention. Limits: 10 MB, 16 MP, 8192 px/side, 240 nodes. Some small or clipped elements can be missed; verify before coding.

The official DeepSeek provider is pinned. Precise mode can align text boxes using existing local Tesseract English OCR, only for nearby high-confidence exact text matches. Ambiguous or unmatched boxes remain estimates; missing OCR does not block analysis. No extra AI call or model download.

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Written by the Elysia Tools team

Tool usage guide

Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Category
AI Tools
Input types
file, select, checkbox, textarea
Output type
json
Sample coverage
4
API ready
Yes

Overview

UI Screenshot to Structured JSON transforms static interface screenshots into editable visual layout trees, element inventories, and coding handoff specifications. Powered by DeepSeek via OpenRouter, the tool parses visual hierarchies, extracts labels and bounding boxes, and exports reviewable JSON and Markdown files.

When to use

  • Auditing legacy application interfaces or static prototypes without access to original design tokens or source code.
  • Extracting component hierarchies, bounding boxes, and visible copy to draft developer handoff documentation.
  • Cataloging visual UI elements and layouts into structured JSON trees for frontend refactoring or design system inventories.

How it works

  1. 1Upload a PNG, JPG, or WebP screenshot up to 10 MB and confirm third-party AI processing consent.
  2. 2Select Quick mode for a rapid single-pass inspection, Precise mode for supplementary regional crop reviews, or offline demo mode.
  3. 3Optionally supply up to 2000 characters of contextual interface descriptions to guide visual parsing.
  4. 4Inspect interactive overlays, edit detected text labels, element types, or bounding boxes, and export the structured JSON or Markdown file.

Use cases

  • Converting legacy software screenshots into clean layout trees for modern frontend component scaffolding.
  • Extracting on-screen copy and navigation hierarchies from design mockups into reviewable JSON structures.
  • Documenting UI component boundaries and parent-child container relationships for developer specifications.

Examples

1. Parsing a SaaS Dashboard Screenshot for Frontend Handoff

Frontend Engineer

Background

A developer needs to rebuild an analytics dashboard layout from a client's static PNG mockup without Figma source files.

Problem

Manually measuring bounding boxes and transcribing card labels from the static screenshot slows down component scaffolding.

How to use

Upload the 1440x900 PNG dashboard, choose Precise mode, and add brief layout context clarifying the two-column structure.

{
  "mode": "precise",
  "language": "en",
  "consent": true,
  "context": "Two-column analytics dashboard with side navigation and metrics grid."
}

Outcome

Receives a structured JSON file containing the parent layout tree, card coordinates, and labeled metric components ready for coding.

2. Exploring Component Extraction with Offline Demo Data

Design Systems Specialist

Background

A design specialist wants to evaluate the schema and structure of exported visual nodes before processing client interface images.

Problem

Needs to review output data fields, bounding box formats, and node nesting without uploading screenshots or calling external APIs.

FAQ

What AI model runs the screenshot visual analysis?

Analysis is executed exclusively by deepseek/deepseek-v4.1-flash-1017 via OpenRouter without model fallback or automatic retries.

What are the image dimension and node limits?

Uploads support images up to 10 MB, 16 MP, and 8192 px per side, generating a maximum layout tree of 240 nodes.

How does Precise mode differ from Quick mode?

Precise mode adds up to two focused crop evaluations and applies local Tesseract English OCR alignment to verify text bounds.

Are exported styles and DOM trees identical to the original code?

No, styles and bounding boxes are visual approximations inferred from screenshot pixels, not production CSS or DOM nodes.

Does the offline demo upload my data or invoke AI endpoints?

No, the offline demo renders synthetic mock data locally without issuing network requests or processing real images.

Hub

UI Screenshot Structure Review and Development Handoff

Turn one authorized UI screenshot into a manually reviewed component tree, validated JSON and development notes, with source coordinates, uncertainty and revision evidence preserved.

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How to use

Select the offline demo mode and explore the pre-rendered layout tree directly.

{
  "mode": "demo",
  "language": "en",
  "consent": false,
  "context": ""
}

Outcome

Immediately generates synthetic workspace layout JSON with node hierarchy, visual types, and bounding coordinates without AI usage.