AI Artwork Text Replacement System Development

A leading US-based e-commerce firm partnered with Rishabh Software to develop an AI-powered artwork text replacement system. The collaboration aimed to automate and scale the manual process of updating text in Adobe Illustrator (AI) files while preserving the original design aesthetics. The primary goal was to enable fast, accurate, and style-consistent text edits while maintaining creative integrity and accelerating design delivery across products.

Capability

AI-Powered Workflow Automation

Industry

E-commerce

Country

USA

Key Features

We developed an AI-powered artwork text replacement system tailored for design to automate and scale the process of updating textual content in Adobe Illustrator files. The solution preserves original styles and layouts while ensuring high-quality replacements.

AI File Parsing

Our team implemented parsing logic using the Adobe Illustrator SDK to extract structured data from artwork files. By applying our expertise in AI/ML development services, we automated text layer identification, font type, size, style, and coordinates, laying the groundwork for accurate text detection and replacement.

TextStyleBrush for Aesthetic Transfer

The solution applies the TextStyleBrush model to replicate text styles with precision. It enables users to edit text in image with AI, ensuring seamless visual continuity by maintaining elements like font weight, color, and orientation across replacements.

Cloud-Based Processing via APIs

A secure API layer processes uploaded files, extracts data, applies AI models, and returns the updated artwork. The architecture enables fast and scalable operations while integrating seamlessly with existing workflows.

Style-Preserving Text Replacement

We integrated deep learning capabilities to identify and replace text content while retaining its visual format. Using a trained model, the system ensures that replaced text matches the font, alignment, and aesthetics of the original.

Key-Value Mapping for Repeated Text

To handle instances where identical text appears in multiple locations, a key-value mapping feature allows users to specify different replacements for each occurrence. This ensures contextual accuracy across the artwork.

User Interface for Text Input

We built a front-end interface where users can upload artwork files, input replacement text, and define key-value mappings. This intuitive UI makes the system accessible for both designers and non-technical users.

Challenges

Manual editing of text in Adobe Illustrator files was time-consuming and error-prone for creative teams.

Designers struggled to maintain font consistency and styling across multiple versions of the same artwork.

The absence of an automated mapping system made it challenging to handle identical text appearing in multiple places with unique replacements.

Lack of intelligent text parsing limited scalability, especially when dealing with high volumes of campaign creatives.

Existing tools offered limited integration with cloud infrastructure, making collaboration and large-scale deployment inefficient.

Solutions

We delivered a purpose-built AI Artwork Text Replacement System to automate Illustrator text editing with high accuracy while preserving original design aesthetics. The system leverages custom-trained deep learning models, intelligent parsing, and Azure-based deployment to provide a scalable, secure, and future-ready platform.

AI-Powered End-to-End Automation

Using the Adobe Illustrator SDK, the system parses AI files to extract text layers along with their fonts, styles, sizes, and coordinates. The pipeline automates file upload to output, ensuring minimal effort and consistent, high-quality results across large creative workloads.

Custom-Trained Deep Learning Models

Our AI models, including TextStyleBrush and deep-text-edit, were trained on a dataset of more than 1,000 real artwork files. This training enables style-preserving text replacement with 90% accuracy, maintaining the original font, color, size, alignment, and layout for each replaced element.

AI-Assisted Text Detection and Semantic Parsing

Beyond basic text extraction, the system applies AI-assisted detection and semantic parsing to identify and structure text elements contextually. This allows precise, context-aware editing, ensuring that replacements fit naturally within the design’s visual flow.

Key-Value Mapping for Repeated Text Instances

We implemented a dynamic key-value mapping logic to manage repeated text elements that require unique replacements. This approach improved contextual accuracy and reduced repetitive editing errors by 60%, especially in significant and multi-variant creative assets.

Secure, Scalable Azure-Based Deployment

The solution was deployed on Azure for maximum scalability and security. Azure Blob Storage handles secure file storage, Azure Functions enable serverless processing, and Azure Machine Learning manages model training and inference. GPU-powered Azure Virtual Machines ensure high-performance processing even for bulk workloads.

Future-Ready Architecture for Generative AI

The platform is designed with extensibility in mind, supporting integration with next-generation generative AI tools such as Stable Diffusion for future capabilities like background inpainting and style-conditioned text rendering.

Outcomes

0 %

reduction in manual editing time for Illustrator files

0 %

accuracy in preserving font styles and layout during replacement

0 %

increase in design team productivity across high-volume projects

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Key Features

AI File Parsing

TextStyleBrush for Aesthetic Transfer

Cloud-Based Processing via APIs

Style-Preserving Text Replacement

Key-Value Mapping for Repeated Text

User Interface for Text Input

Challenges

Solutions

AI-Powered End-to-End Automation

Custom-Trained Deep Learning Models

AI-Assisted Text Detection and Semantic Parsing

Key-Value Mapping for Repeated Text Instances

Secure, Scalable Azure-Based Deployment

Future-Ready Architecture for Generative AI

Outcomes

Technologies Used

Project Snapshots

Recent Case Studies

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