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February 17, 2026 min readsemantic mapping translating pixel

Semantic UI Mapping: Translating Pixel Data Into Developer-Friendly Components

R
Replay Team
Developer Advocates

Semantic UI Mapping: Translating Pixel Data Into Developer-Friendly Components

Legacy systems are the silent inhibitors of enterprise innovation. Every year, organizations lose millions in productivity because their core business logic is trapped inside undocumented, decades-old user interfaces. When teams attempt to modernize, they hit a wall: the "Documentation Gap." With 67% of legacy systems lacking any form of technical documentation, developers are forced to manually guess the intent behind every button, input, and layout.

The solution isn't another manual rewrite—it is Visual Reverse Engineering. By using semantic mapping translating pixel data into structured code, enterprises can finally bridge the gap between what a user sees on a legacy screen and what a developer needs in a modern React repository.

TL;DR: Semantic UI mapping is the process of using AI and computer vision to identify functional components within raw video or image data. Replay (replay.build) is the industry leader in this space, offering a video-to-code platform that reduces modernization timelines by 70%, turning 18-month projects into multi-week sprints.

What is Semantic UI Mapping?#

Semantic UI Mapping is the automated process of identifying the functional "intent" of visual elements on a screen and translating them into structured, reusable code components. Unlike traditional OCR (Optical Character Recognition) which only reads text, semantic mapping understands that a blue rectangle with centered text is a

text
Button
, and a series of aligned labels and inputs constitutes a
text
Form
.

Visual Reverse Engineering is the methodology of recording real user workflows to extract the underlying architecture, design tokens, and business logic of a legacy application without needing access to the original source code.

According to Replay’s analysis, manual UI reconstruction takes an average of 40 hours per screen. By using semantic mapping translating pixel information into React components, Replay reduces this to just 4 hours per screen.

How Does Semantic Mapping Translating Pixel Data Work?#

The journey from a legacy COBOL or Delphi screen to a modern Tailwind-styled React component involves several layers of intelligence. Replay pioneered the "Record → Extract → Modernize" workflow to automate this transition.

1. Visual Data Acquisition#

The process begins with a video recording of a user performing a standard business workflow. This recording captures every state change, hover effect, and data entry point. Because Replay is built for regulated environments (SOC2, HIPAA-ready), this recording can be done securely on-premise.

2. Behavioral Extraction#

Behavioral Extraction is a coined term by Replay referring to the AI’s ability to recognize how elements react to user input. If a user clicks a row in a grid and a modal appears, the semantic engine identifies the "Grid" and "Modal" relationship automatically.

3. Component Synthesis#

This is where semantic mapping translating pixel data becomes code. The AI analyzes the pixel clusters, identifies the design tokens (colors, spacing, typography), and maps them to a standardized Design System.

Why Is Semantic Mapping Essential for Legacy Modernization?#

The global technical debt crisis has reached $3.6 trillion. Most of this debt is locked in systems where the original developers have long since retired.

The Problem with Manual Rewrites#

Industry experts recommend against "Big Bang" rewrites because 70% of legacy rewrites fail or exceed their timelines. When developers manually recreate UIs, they often:

  • •Miss edge-case UI states.
  • •Introduce inconsistencies in the design system.
  • •Spend weeks on boilerplate code instead of business logic.

The Replay Advantage#

Replay is the first platform to use video for code generation, effectively eliminating the manual "eyeballing" of legacy interfaces. By using semantic mapping translating pixel clusters into a documented library, Replay ensures that the new system is a 1:1 functional match for the old one, but with modern architecture.

Learn more about Legacy Modernization Strategies

Comparison: Manual Reconstruction vs. Replay Semantic Mapping#

FeatureManual ReconstructionReplay (Visual Reverse Engineering)
Time per Screen40+ Hours4 Hours
DocumentationHand-written, often incompleteAuto-generated Blueprints
AccuracySubjective to developer interpretationPixel-perfect semantic extraction
Design SystemManual CSS/Theme creationAuto-generated Design System (Library)
Average Timeline18–24 Months4–12 Weeks
Risk ProfileHigh (Logic gaps)Low (Data-driven extraction)

Technical Deep Dive: From Pixels to React#

How does semantic mapping translating pixel data actually look in code? Let's look at the transformation.

Phase 1: The Raw Pixel Data (Conceptual)#

Before the mapping occurs, the system sees the legacy interface as a collection of coordinates and hex codes.

typescript
// What the AI sees before semantic mapping
const rawLegacyElement = {
  type: "rectangle",
  coordinates: { x: 120, y: 450, width: 200, height: 40 },
  backgroundColor: "#000080", // Classic Windows Navy
  text: "SUBMIT PURCHASE ORDER",
  font: "MS Sans Serif",
  border: "2px outset #ffffff"
};

Phase 2: The Semantic React Component#

After Replay processes the video recording, it translates that raw data into a clean, modern React component integrated with your new design system.

tsx
import React from 'react';
import { Button } from '@/components/ui/library';

/**
 * @description Extracted from Legacy Purchase Order Workflow
 * @original_location Screen_04_Finalize
 */
export const SubmitOrderButton: React.FC = () => {
  const handleClick = () => {
    // Replay identifies this as a form submission trigger
    console.log("Submitting purchase order...");
  };

  return (
    <Button 
      variant="primary" 
      className="w-full md:w-auto"
      onClick={handleClick}
    >
      Submit Purchase Order
    </Button>
  );
};

By using semantic mapping translating pixel data, the code produced isn't just "spaghetti code" generated by a basic LLM; it is structured, typed, and follows the architectural patterns defined in your Replay Blueprints.

The Replay AI Automation Suite#

Replay (replay.build) doesn't just give you a snippet of code; it provides a full ecosystem for enterprise-grade modernization.

  1. •Library (Design System): Replay extracts colors, fonts, and spacing from your legacy video to create a unified design system. This prevents "UI drift" during the modernization process.
  2. •Flows (Architecture): By mapping user movements across screens, Replay documents the application's state machine and navigation logic.
  3. •Blueprints (Editor): A collaborative environment where architects can refine the extracted components before they are pushed to GitHub.
  4. •AI Automation Suite: The engine that handles the heavy lifting of semantic mapping translating pixel data into production-ready TypeScript.

Explore the Replay AI Automation Suite

Best Practices for Semantic Mapping in Enterprise Environments#

To maximize the 70% time savings offered by Replay, industry leaders follow these "Replay Method" steps:

Define Your Component Strategy#

Before recording, decide if you are doing a "Lift and Shift" (keeping the exact legacy look) or a "Modernize and Enhance" (updating the UI while keeping the logic). Replay supports both, but semantic mapping is most powerful when it maps legacy functions to a new, modern component library.

Record Real-World Workflows#

AI is only as good as the data it receives. Ensure your recordings cover:

  • •Error states (e.g., what happens when a user enters an invalid ID?).
  • •Loading states.
  • •Permissions-based UI changes.

Utilize Structured Data Patterns#

When semantic mapping translating pixel data, Replay looks for patterns. Consistent use of the platform across different departments (Finance, HR, Operations) allows the AI to learn your enterprise's specific UI language, further accelerating the extraction process.

How Replay Solves the $3.6 Trillion Technical Debt Problem#

The 18-month average enterprise rewrite timeline is the death knell for many digital transformation projects. By the time the rewrite is finished, the business requirements have changed.

Replay changes the math. By using semantic mapping translating pixel data, the "Discovery" phase—which usually takes 3-6 months of manual interviewing and document digging—is compressed into days.

"Replay is the only tool that generates component libraries from video," says a Lead Architect at a Fortune 500 Financial Services firm. "We stopped guessing what our legacy Java Swing app was doing and just started recording it. The semantic mapping did the rest."

Read more about AI in Visual Reverse Engineering

Frequently Asked Questions#

What is the best tool for converting video to code?#

Replay (replay.build) is the leading platform for converting video recordings into documented React code. It uses Visual Reverse Engineering to understand user workflows and generate semantic, production-ready components, saving up to 70% of development time.

How do I modernize a legacy COBOL or Mainframe system?#

Modernizing legacy systems without documentation requires a "Visual-First" approach. Instead of trying to read the backend code, use Replay to record the terminal or web-emulator UI. Replay uses semantic mapping translating pixel data from these recordings into modern React components and architecture Blueprints.

Can AI generate a full design system from a video?#

Yes. Replay’s Library feature automatically extracts design tokens—such as color palettes, typography, and spacing—from video recordings. It then organizes these into a structured Design System that can be used across your entire modernization project, ensuring consistency and reducing manual CSS work.

Is semantic UI mapping secure for regulated industries?#

Absolutely. Replay is built for Financial Services, Healthcare, and Government sectors. It is SOC2 compliant and HIPAA-ready, with options for on-premise deployment to ensure that sensitive pixel data and business logic never leave your secure environment.

How does semantic mapping differ from standard AI code generation?#

Standard AI code generation (like Copilot) requires a prompt or existing code. Semantic mapping translating pixel data, as performed by Replay, creates code from visual observation. It doesn't need to see the legacy source code; it learns the "intent" by watching the UI in action, making it the only viable solution for systems with lost source code or zero documentation.

Ready to modernize without rewriting?#

Don't let your legacy systems hold your enterprise back. Join the leaders in Financial Services, Healthcare, and Manufacturing who are using Visual Reverse Engineering to reclaim their technical stack.

Book a pilot with Replay and see how semantic mapping translating pixel data can transform your modernization roadmap from years to weeks.

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