SVG AI

Introduction

Vector processing and bitmap-to-SVG conversion represent a persistent technical challenge in digital graphics. Bitmap images — photographs, scans, screenshots — are stored as pixels, without structural information about the shapes, contours, or objects they contain. The SVG (Scalable Vector Graphics) format, conversely, describes an image as a set of geometric primitives — lines, Bézier curves, circles, polygons — linked by precise mathematical relationships. Converting a pixel representation to a vector representation therefore requires complex inverse analysis: extracting geometric structure from a grid of pixels, an operation that the human brain performs intuitively but remains an open problem in computer vision.

The limitations of existing tools manifest at several levels. Manual graphics editors like Adobe Illustrator or Inkscape offer automatic vectorization tools, but their results are often disappointing: generated paths contain an excessive number of control points, curves are approximate, and colors are poorly rendered. A simple line drawing can produce an SVG of several hundred kilobytes, riddled with artifacts and difficult to edit afterward. Online automatic solutions, meanwhile, sacrifice quality for speed: they produce low-end vectorizations, without optimization, without understanding of the image's semantic content, and without the possibility of integration into professional pipelines. None of these approaches can distinguish a logo from a photograph, a technical diagram from an artistic illustration — yet each image type requires a radically different vectorization strategy.

SVG AI addresses these limitations by providing an MCP (Model Context Protocol) service specialized in vector processing and bitmap-to-SVG conversion. The service exposes capabilities for intelligent vectorization, path simplification, SVG code optimization, and multi-format conversion via the MCP protocol. It distinguishes itself from existing solutions through its layered architecture combining computer vision, geometric analysis, artificial intelligence, and automatic validation. Each pipeline step is specialized and optimized for a specific aspect of the problem, ensuring result quality suitable for professional use.

SVG AI's architecture relies on six interconnected processing layers. The first, acquisition and preprocessing, prepares the source image for vector analysis. Steps include resizing while preserving aspect ratio, conversion to grayscale or adapted color spaces, noise reduction through Gaussian or median filtering, contrast and brightness adjustment, adaptive binarization to separate foreground from background, and detection and correction of acquisition defects such as skew or blur. OpenCV is the backbone of this layer, offering a comprehensive library of proven image processing algorithms.

The second layer, computer vision and geometry analysis, examines the preprocessed image to extract a structured representation. It detects edges through algorithms like Canny or Sobel, identifies elementary geometric shapes through Hough transform (lines, circles, ellipses), segments the image into homogeneous regions through region growing or watershed, extracts skeletons and medians for elongated shapes, and analyzes object topology — parent-child, adjacency, and inclusion relationships between detected shapes. This analysis produces a semantic map of the image that will serve as the basis for vector tracing.

The third layer, vector tracing proper, converts the analysis map into SVG primitives. It begins with polygonal approximation of detected contours using the Douglas-Peucker algorithm to reduce the number of points while preserving visual fidelity. Bézier curves are then fitted to contour segments through least-squares regression, with control of tangency and continuity at junctions. The layer handles closed shape reconstruction, hole and counter-form detection, and visual attribute assignment — fill and stroke colors, gradients, opacity. Each generated primitive is stored with confidence metadata indicating the quality of the fit.

The fourth layer, LLM optimization, is SVG AI's central innovation. A specialized Mistral model — fine-tuned on a corpus of millions of SVG files — analyzes the raw vector trace to detect geometric errors: redundant control points, degenerate curves (zero length), self-intersections, tangent discontinuities, open unclosed shapes. The LLM restructures the SVG by grouping primitives by functional role — background, borders, patterns, text — and removing redundant or unnecessary elements. It substitutes complex path combinations with simpler SVG primitives when possible: a circle approximated by 16 Bézier points is replaced by a native <circle> tag, a rectangle by a <rect> tag, a complex path by a combination of standard primitives. Finally, the LLM enriches the SVG with semantic metadata: identification of represented objects (logo, icon, diagram, text), layer and group annotation, CSS class name suggestions, and logical document structuring.

This LLM optimization brings concrete benefits compared to purely algorithmic solutions. SVG file size is reduced by an average of 40 to 60% compared to classic Potrace vectorization, thanks to primitive substitution and intelligent elimination of superfluous points. SVG code readability is considerably improved: the produced file is structured, commented, and follows domain conventions, making it editable by humans. Visual quality is preserved or even improved, as the LLM corrects geometric defects that traditional algorithms do not detect: curve joins invisible to the naked eye but generating micro-defects at display, suboptimally placed control points, near-circular shapes not recognized as circles.

Fine-tuning the Mistral model on a specialized SVG corpus is a key aspect of system performance. The training corpus includes millions of SVG files from varied sources: open source icon libraries, technical diagrams, logos, vector illustrations, maps and plans. Each file is accompanied by its bitmap rendering at different resolutions, allowing the model to learn the relationship between a pixel image and its optimal vector representation. Fine-tuning targets several objectives: geometric error correction, SVG code restructuring, primitive substitution, metadata generation, and size optimization. The model also learns to recognize recurring patterns — borders, background patterns, symmetric repetitions — and represent them compactly with specific SVG elements like <pattern>, <use>, or <symbol> tags.

The fifth layer, validation and quality control, guarantees the reliability of the produced result. It implements visual comparison between the source bitmap image and the rendered generated SVG, calculating objective metrics: PSNR (Peak Signal-to-Noise Ratio) measures pixel-by-pixel fidelity, SSIM (Structural Similarity Index) evaluates preservation of perceived visual structure, and MSE (Mean Squared Error) quantifies average quadratic error. Acceptance thresholds are configurable according to use: logo vectorization requires SSIM above 0.98, while a technical diagram may tolerate a lower threshold. The layer also detects structural anomalies — missing primitives, excessive deformations, color inversions, position shifts — and verifies the generated SVG code's conformance to W3C specifications, including XML syntax validity, absence of deprecated attributes, and adherence to accessibility best practices. A composite quality score is calculated from all these metrics, enabling ranking of each result and triggering automatic reprocessing if the score is insufficient.

The sixth layer, indexing and search, enables leveraging produced SVGs as a reusable vector library. Each SVG is indexed with its semantic metadata — dominant shapes, colors, content type, complexity — in a vector database. The search engine allows finding an SVG by visual similarity: from a query image, the system finds the closest SVGs in latent space. Textual description search is also supported, thanks to semantic embeddings generated by the LLM. This layer transforms SVG AI into a vector asset management system for design and technical documentation teams.

The orchestration and workflow layer via MCP constitutes SVG AI's backbone. The MCP protocol exposes each capability — vectorization, optimization, validation, indexing, search — as a callable tool in a standardized way. Workflows are defined as sequences of tools with configurable parameters: a typical full vectorization workflow chains acquisition, analysis, tracing, LLM optimization, validation, and indexing in a single MCP request. Partial workflows allow calling a specific step, for example optimization alone on an existing SVG. The orchestrator manages the queue, processing parallelization, error recovery, and result notification. Each workflow exposes performance metrics — processing time, result quality, LLM cost — queryable via dedicated MCP endpoints.

SVG AI use cases cover a broad spectrum of professional applications. Technical diagram digitization is a recurring need in industry and engineering: architectural plans, electrical schematics, mechanical diagrams, process maps. These documents often exist in paper format or as scanned images, and their vectorization enables integration into technical management systems, interactive manuals, or evolving documentation. SVG AI preserves line precision, annotation hierarchy, and technical symbol readability, where generic solutions produce confusing traces and illegible labels.

Logo and illustration vectorization benefits from SVG AI's ability to understand an image's semantic structure. A logo is not a simple collection of shapes — it is a coherent set of symbols, stylized text, and graphic elements that must be reproduced with absolute fidelity. SVG AI identifies text areas, repeated patterns, symmetries, and alignments, and produces a production-ready SVG for print, web, or signage. For plans and maps, the service handles vectorization of high-density information images: geographic maps, floor plans, infrastructure schematics, organizational charts. The LLM layer can restore a coherent visual hierarchy, with distinct stroke thicknesses for main and secondary arteries, uniform fill colors for homogeneous areas, and readable typography for labels.

Technical benefits of SVG AI are measurable at several levels. Vectorization quality surpasses classic algorithms thanks to LLM optimization that corrects structural defects and substitutes primitives. SVG file size is reduced by 40 to 60%, improving load and rendering performance in browsers and applications. Semantic structuring of SVG code makes it usable and modifiable by design teams, unlike the unreadable output of automatic vectorizers. MCP integration enables using SVG AI in any pipeline, without graphical interface, without human interaction, and with complete operation traceability.

Operational benefits are equally significant. Batch processing and workflow orchestration automate vectorization chains that previously required hours of manual work in graphics editors. Automatic validation guarantees a consistent quality level across all productions, without human intervention. Indexing and search facilitate reuse of vector assets, avoiding vectorizing the same source image multiple times. Deployment as an MCP service enables integrating SVG AI into CI/CD environments: graphic assets are automatically vectorized during builds, with alerts in case of quality degradation. Finally, the agent approach offers horizontal scalability: the service can be duplicated to handle large volumes, with a queue and load distribution managed by the orchestrator.

Conclusion

The project's evolution prospects are promising. Continuous improvement of the specialized Mistral SVG model through fine-tuning on enriched corpora will achieve even higher quality levels, particularly for difficult cases like complex gradients, textures, drop shadows, and transparencies. Extension to vector formats other than SVG — EPS, PDF, AI — is under study to cover editorial chain and print workflow needs. SVG generation from textual descriptions, without source image, is a natural evolution being explored by the lab: describing a diagram in natural language and receiving a structured, ready-to-use SVG. Finally, integration with graphic design assistants will enable a round-trip between AI and the human designer: the AI proposes a vectorization, the designer adjusts, the AI optimizes — a human-machine collaboration combining automatic processing power with creative human expertise.