Abstract: Systems and methods are disclosed for model based document image enhancement. Instead of requiring paired dirty and clean images for training a model to clean document images (which may cause privacy concerns), two models are trained on the unpaired images such that only the dirty images are accessed or only the clean images are accessed at one time. One model is a first implicit model to translate the dirty images from a source space to a latent space, and the other model is a second implicit model to translate the images from the latent space to clean images in a target space. The second implicit model is trained based on translating electronic document images in the target space to the latent space. In some implementations, the implicit models are diffusion models, such as denoising diffusion implicit models based on solving ordinary differential equations.

Abstract: A method may include extracting training tokens from a training corpus, generating training token features and training contextual features for each of the training tokens, training a probabilistic graphical model using classes, the training token features, and the training contextual features, extracting, from actual data, actual tokens including an actual token, generating actual token features and actual contextual features for each of the actual tokens, and classifying the actual token as a class using the probabilistic graphical model, the actual token features, and the actual contextual features.

Abstract: A method and system for generating synthetic form image involves obtaining a multitude of field value data and associated field labels for a chosen type of form document from an electronic data source, classifying the multitude of field value data into a multitude of data categories, where the multitude of data categories, learning statistical data distributions for categorical and numerical data types using the classified categorical and numerical data, and sampling data elements randomly using the learned data distributions to generate synthetic data for categorical and numerical data. The method also involves assembling the synthetic data for the multitude of data categories with the associated field labels to generate a labeled synthetic textual data set, rendering the labeled synthetic textual data set over a structured form layout image to produce a synthetic form image, and storing the synthetic form image and the labeled synthetic textual data set.

Abstract: A method may include extracting training tokens from a training corpus, generating training token features and training contextual features for each of the training tokens, training a probabilistic graphical model using classes, the training token features, and the training contextual features, extracting, from actual data, actual tokens including an actual token, generating actual token features and actual contextual features for each of the actual tokens, and classifying the actual token as a class using the probabilistic graphical model, the actual token features, and the actual contextual features.