VATr++: Advanced Few-Shot Styled Handwritten Text Generation

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Feb 23 2024

In an article recently submitted to the ArXiv* server, researchers investigated the impact of visual and textual input on styled handwritten text generation (HTG) models.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.

They proposed strategies for input preparation and training regularization, validated through extensive analysis across different settings and datasets. Additionally, the researchers standardized the evaluation protocol for HTG and conducted comprehensive benchmarking of existing approaches to facilitate fair comparisons and foster progress in the field.

Related Work

Past work in styled HTG has focused on generating diverse, high-quality training data for handwriting-related tasks and aiding physically impaired individuals in creating handwritten notes. While offline approaches have gained popularity due to their efficiency in capturing handwriting style from static images, they face challenges in rendering long-tail and out-of-charset characters. Recent advancements, such as the visual archetype transformer (VATr), have addressed some of these issues but still need help generating scarce characters faithfully.

Few-Shot Styled HTG Overview

This study focuses on the few-shot-styled variant of offline HTG, where a limited number of handwritten samples are available for a specific writer of interest. These samples, denoted as Xw, are P images containing handwritten words. Additionally, researchers consider a set of Q words denoted as C={ci}Qi=0 of arbitrary length. The objective is to generate Q images YCw containing the phrase in C rendered in the style of writer w. Researchers utilize a hybrid convolutional-transformer architecture combined with VATr for content representation, building upon previous work. This architecture is extended with novel input processing and training strategies to enhance performance.

The proposed architecture comprises a style encoder that converts style samples Xw into style features Sw, combining a convolutional encoder and a transformer encoder. Pre-training the convolutional backbone on a significant synthetic dataset aid in robust feature extraction from the style samples. Modifications are also introduced in the style input preparation, aiming to resolve ambiguity and inconsistency issues by treating punctuation marks as standalone words in the dataset.

The content-guided decoder consists of a multi-layer, multi-head decoder performing self-attention among content vectors and cross-attention between content and style vectors. Visual archetypes, derived from rendering characters using the unifont font, represent content queries. It allows the model to leverage geometric similarities among characters for more faithful rendering, especially of long-tail characters. Furthermore, text input preparation is enhanced through a specific augmentation scheme, balancing the occurrence of rare characters in the training corpus, thus improving the model's ability to generate these characters faithfully. Overall, these architectural enhancements and training strategies contribute to the enhanced performance of the HTG model, facilitating more accurate rendering of handwritten text in the style of a given writer, even for rare characters and complex textual content.

HTG Evaluation Protocol Overview

Standardization of the evaluation process is crucial for objectively assessing the performance of various HTG approaches. A consistent protocol is necessary to compare different methods effectively. Therefore, establishing a straightforward evaluation procedure ensures reliable and transparent assessments, fostering improvements in HTG models.

Researchers have designed a proposed evaluation protocol to comprehensively assess the performance of HTG models, addressing this need effectively. For clarity, the description refers to the IAM dataset, a widely used benchmark in HTG research. The IAM dataset comprises handwritten text samples from 657 writers, split into training and test sets. The protocol covers various scenarios:

In each scenario, researchers define sets of in-vocabulary and out-of-vocabulary words. Researchers define sets of in-vocabulary and out-of-vocabulary words in each scenario. They included a test scenario where the model replicates the test set, generating images iteratively with reference styles from the same writers but with different words.

After generation, evaluation involves comparing the generated images with real images using metrics like Frechet inception distance (FID), kernel inception distance (KID), and handwriting distance (HWD) to measure visual and calligraphic style similarity. This standardized evaluation protocol ensures consistent and fair assessments, facilitating advancements in HTG research.

Experimentally validating the proposed approach involves comparing it quantitatively with state-of-the-art methods on the IAM dataset. Generalization capabilities to unseen words, styles, and datasets, including rare character generation, are also explored. The complete HTG model is trained on the IAM dataset using specific optimization strategies and architectural choices. The model is trained for a fixed number of epochs, evaluating performance regularly.

Comparison against several state-of-the-art HTG approaches considers multiple evaluation metrics and dataset variants. The results demonstrate the effectiveness of the strategy across different scenarios and datasets. Assessing the model's ability to generalize to unseen words, styles, and datasets highlights its robustness and capacity to generate realistic text images across diverse conditions.

Conducting an ablation study analyzes the impact of individual strategies proposed in the model. It helps identify critical components contributing to performance enhancement, providing insights for future research directions. Overall, the proposed evaluation protocol and experimental findings contribute to advancing the field of HTG by providing a standardized framework for evaluation and highlighting the approach's strengths.

Conclusion

To sum up, the work addressed the limitations in the current style of HTG research by extending the VATr architecture to VATr++, focusing on improving rare character generation and handwriting style capture. The work proposed specific input preparation and training techniques and introduced a standardized evaluation protocol to enhance model performance and facilitate fair comparisons. The experiments demonstrated the effectiveness of VATr++ in generating styled handwriting images across various scenarios and datasets, surpassing competitors, particularly in rare character generation.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.