Generation of training data for handwritten text recognition using latent diffusion models
Abstract
Handwritten documents have served as the predominant medium for the preservation and transmission of information. Numerous works aim at implementing automatic searchability and information extraction from such documents. Additionally, a research field has emerged that deals with the generation of handwritten words and documents. The task of handwritten text generation (HTG) constitutes the generation of a realistic looking image depicting a given string in a handwriting with a desired style. In particular, the correctness and readability of the generated word as well as the imitation of the desired style pose major challenges. Generative models such as generative adversarial networks and diffusion models have been adopted to approach this task. HTG offers the promising opportunity to generate annotated training data for other document analysis models. This is particularly interesting when training data is generated for a new target dataset for which no annotated examples are available. In this thesis, an HTG system based on a latent diffusion model for the generation of training data for handwritten text recognition models is proposed. In order to generate images for an unseen target dataset, a pretrained masked autoencoder is used to extract style encodings from a set of example images. Together with embeddings of the string to be generated, these encodings are used to condition the generation process using classifier-free guidance. In order to enhance the generation…
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