STEFANN: Scene Text Editor using Font Adaptive Neural Network (1903.01192v3)

Abstract: Textual information in a captured scene plays an important role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advantages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the problem in two stages. At first, the unobserved character (target) is generated from an observed character (source) being modified. We propose two different neural network architectures - (a) FANnet to achieve structural consistency with source font and (b) Colornet to preserve source color. Next, we replace the source character with the generated character maintaining both geometric and visual consistency with neighboring characters. Our method works as a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR datasets both qualitatively and quantitatively.

• The paper presents a novel character-level text editing approach that synthesizes target characters using a font-adaptive neural network.
• It leverages FANnet for structural consistency and Colornet for accurate color transfer, avoiding reliance on error-prone recognition modules.
• Experimental results using SSIM on COCO-Text and ICDAR datasets validate its effectiveness in document restoration and OCR-free text editing.

An Analysis of "STEFANN: Scene Text Editor using Font Adaptive Neural Network"

The paper "STEFANN: Scene Text Editor using Font Adaptive Neural Network" introduces a novel approach for editing text directly within images. This approach stands out because it allows modification at a character-level, which is a significant departure from the focus of prior works that predominantly emphasized text detection and recognition. This research details a method comprising two stages that involve generating unobserved target characters from observed source characters and subsequently integrating these new characters into their respective positions within the image, adhering to both geometric and visual consistency.

Methodological Overview

The proposed solution involves two key components: FANnet and Colornet. FANnet is tasked with the generation of target characters: it utilizes neural net architecture to adapt to the font features of a single observed source character and produces other necessary characters while maintaining structural consistency. Colornet’s responsibility is to transfer the color attributes from the source to the target character, thus ensuring that the visual style of the original text block is preserved.

1. FANnet (Font Adaptive Neural Network): FANnet uses convolutional and fully connected layers to generate characters that match the style of a single observed character. This approach obviates the need for explicit text recognition, sidestepping the heavy dependency on text recognition modules that are inherently error-prone, especially in complex scenarios typical in natural scene text images.
2. Color Transfer with Colornet: Colornet efficiently transfers the color scheme from the source character to the generated target character. This system overcomes the hurdles associated with traditional color transfer techniques, which often falter when dealing with localized character regions rich in texture and gradient color patterns.

Experimental Results

The effectiveness of this method is quantitatively and qualitatively demonstrated using extensive tests conducted on COCO-Text and ICDAR datasets. A familiar metric, Structural Similarity Index (SSIM), among others, is used to evaluate the visual consistency of the generated images. The results indicate that FANnet generates images with a favorable average SSIM, and Colornet ensures accurate color transfer, maintaining visual consistency across a range of scenarios including dark scenes and text blocks with perspective distortion.

Furthermore, through a series of ablation studies, the paper explores the intricacies of the FANnet architecture, demonstrating that each layer contributes significantly to the performance of the generative model. The final architecture, without exclusion of layers, achieves the highest ASSIM scores demonstrating its capability in capturing and replicating fine font characteristics even with diverse input fonts.

Implications and Future Directions

The implications of this work are manifold. Practically, STEFANN could be deployed in document restoration, error correction in textual overlays on imagery, or enhancing the visual accessibility of historical texts by adapting fonts to be more legible. On a theoretical level, this research advances our understanding of integrating style and content generation within neural networks, particularly in scenarios with limited styled data points.

Looking forward, the research opens avenues for further exploration in other text styles and textures, possibly involving more complex generative models or integrating with OCR systems-driven preprocessing steps to handle text image scenarios that involve extensive occlusion or rotation. There could also be potential explorations into zero-shot generative solutions that could handle an even broader spectrum of fonts and colors without dedicated training.

The paper contributes meaningfully to the discipline by addressing a nuanced aspect of scene text analysis and manipulation, demonstrating the robustness and flexibility of adaptive neural networks in handling real-world complexities associated with text in imagery.