Home AI News VATr: Innovating Handwritten Text Generation with Visual Archetypes for Stunning Results

VATr: Innovating Handwritten Text Generation with Visual Archetypes for Stunning Results

VATr: Innovating Handwritten Text Generation with Visual Archetypes for Stunning Results

Styled Handwritten Text Generation (HTG): Creating Personalized Handwritten Text Images

The field of Styled Handwritten Text Generation (HTG) has gained prominence as it aims to replicate the unique calligraphic style of individual writers. This research area has practical applications in generating high-quality training data for personalized Handwritten Text Recognition (HTR) models and creating handwritten notes for individuals with physical impairments. Additionally, the style representations acquired from these models can be useful in tasks such as writer identification, signature verification, and manipulation of handwriting styles.

When it comes to styled handwriting generation, relying solely on style transfer is not enough. Emulating the calligraphy of a particular writer goes beyond considering texture aspects like background and ink color. It involves paying attention to stroke thickness, slant, skew, roundness, individual character shapes, and ligatures. Proper handling of these visual elements is essential to avoid any unintentional changes in content, such as adding or removing strokes.

To address this challenge, specialized methodologies have been developed for HTG. One approach considers handwriting as a trajectory composed of individual strokes, while another approach treats it as an image that captures its visual characteristics. The former approach, known as online HTG, predicts the pen trajectory point by point. The latter approach, called offline HTG, directly generates complete textual images. This article focuses on the offline HTG paradigm due to its advantages.

Unlike the online approach, the offline HTG paradigm does not require expensive pen-recording training data. This makes it suitable for scenarios where information about an author’s online handwriting is not available, such as historical data. Furthermore, the offline paradigm is easier to train and avoids issues like vanishing gradients, allowing for parallelization.

The architecture used in this study, called VATr (Visual Archetypes-based Transformer), introduces a new approach to Few-Shot-styled offline Handwritten Text Generation (HTG). This approach represents characters as continuous variables and uses them as query content vectors within a Transformer decoder. The process starts with character representation, where characters are transformed into continuous variables and used as queries in the Transformer decoder. The decoder plays a crucial role in generating stylized text images based on the provided content.

One notable advantage of this methodology is its ability to generate characters that are less commonly encountered in the training data, such as numbers, capital letters, and punctuation marks. This is possible by utilizing the proximity in the latent space between rare symbols and more frequently occurring ones. The architecture employs the GNU Unifont font to render characters as 16×16 binary images, capturing the visual essence of each character. The dense encoding of these character images is then learned and incorporated into the Transformer decoder as queries. These queries guide the decoder’s attention to the style vectors, which are extracted by a pre-trained Transformer encoder.

The methodology also benefits from a pre-trained backbone, initially trained on a synthetic dataset that emphasizes calligraphic style attributes. Although this technique is often disregarded in HTG, it proves effective in generating robust style representations, especially for styles that have not been seen before. The VATr architecture was validated through extensive experimental comparisons with recent state-of-the-art generative methods.

In summary, VATr is a novel AI framework for handwritten text generation from visual archetypes. If you’re interested in learning more about it, check out the Paper and GitHub links provided. Join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter to stay updated on the latest AI research news and projects.

Source link


Please enter your comment!
Please enter your name here