# Text2QR: Harmonizing Aesthetic Customization and Scanning Robustness for Text-Guided QR Code Generation

Source: [Text2QR: Harmonizing Aesthetic Customization and Scanning Robustness for Text-Guided QR Code Generation](<https://www.sota2.com/research/paper/68fa72821e209131cea56e4b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-02T05:09:39.243Z

Authors: Guangyang Wu, Xiaohong Liu, Jun Jia, Xuehao Cui, Guangtao Zhai

Published: 2024-03-11T06:03:31.000Z

## Abstract

In the digital era, QR codes serve as a linchpin connecting virtual and physical realms. Their pervasive integration across various applications highlights the demand for aesthetically pleasing codes without compromised scannability. However, prevailing methods grapple with the intrinsic challenge of balancing customization and scannability. Notably, stable-diffusion models have ushered in an epoch of high-quality, customizable content generation. This paper introduces Text2QR, a pioneering approach leveraging these advancements to address a fundamental challenge: concurrently achieving user-defined aesthetics and scanning robustness. To ensure stable generation of aesthetic QR codes, we introduce the QR Aesthetic Blueprint (QAB) module, generating a blueprint image exerting control over the entire generation process. Subsequently, the Scannability Enhancing Latent Refinement (SELR) process refines the output iteratively in the latent space, enhancing scanning robustness. This approach harnesses the potent generation capabilities of stable-diffusion models, navigating the trade-off between image aesthetics and QR code scannability. Our experiments demonstrate the seamless fusion of visual appeal with the practical utility of aesthetic QR codes, markedly outperforming prior methods. Codes are available at \\url{https://github.com/mulns/Text2QR}

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [QR code scanning on 100 generated aesthetic QR codes 5 (test)](<https://www.sota2.com/research/sota/qr-code-scanning-on-100-generated-aesthetic-qr-codes-5-test.md>): QR code scanning; 100 generated aesthetic QR codes 5 (test); Accuracy: 100.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.06452>)

[PDF](<https://arxiv.org/pdf/2403.06452.pdf>)

[Implementation](<https://github.com/mulns/Text2QR>)