Simulation of label data to optimize the visual document understanding by using PDFs annotation aware methodology
AssigneeDell Products L.P.
InventorsAtul Kumar, Adya Jha, Saurabh Jha
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Abstract
One example method includes obtaining a text-based document, identifying annotations in the text-based document, and retaining those annotations, converting the text-based document to an image, processing the image, creating simulated label data by integrating the processed image with the annotations, and using the processed simulated label data to train a machine learning model of an OCR (optical character recognition) system. The processed image is a lower quality version of the text-based document that was used to create the image.
Claims20
A method, comprising:
- obtaining a text-based document;
- identifying annotations in the text-based document, and retaining those annotations as native document annotations that exist in the text-based document prior to conversion of the text-based document to an image;
- converting the text-based document to an image;
- processing the image using one or more image degradation operations selected to simulate low-quality scanned documents, the processing including at least one of document smoothening, noise introduction, random patch smoothening, or edge detection and removal;
- creating simulated label data by integrating the processed image with the annotations as training labels by transforming the retained annotations to correspond to the processed image while preserving spatial and semantic alignment between the annotations and document content despite the image degradation;
- and
- using the processed simulated label data to train a machine learning model of an OCR (optical character recognition) system using the annotations as ground-truth labels for the degraded image.
A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:
- obtaining a text-based document;
- identifying annotations in the text-based document, and retaining those annotations as native document annotations that exist in the text-based document prior to conversion of the text-based document to an image;
- converting the text-based document to an image;
- processing the image using one or more image degradation operations selected to simulate low-quality scanned documents, the processing including at least one of document smoothening, noise introduction, random patch smoothening, or edge detection and removal;
- creating simulated label data by integrating the processed image with the annotations as training labels by transforming the retained annotations to correspond to the processed image while preserving spatial and semantic alignment between the annotations and document content despite the image degradation;
- and
- using the processed simulated label data to train a machine learning model of an OCR (optical character recognition) system using the annotations as ground-truth labels for the degraded image.
Dependencies are inferred from the claim text.