Training optical character detection and recognition models for robotic process automation
AssigneeUiPath, Inc.
InventorsDorin Andrei Laza, Trong Canh Nguyen
Life of this patent
Estimating the term…
Abstract
Techniques for training an optical character recognition (OCR) model to detect and recognize text in images for robotic process automation (RPA) are disclosed. A text detection model and a text recognition model may be trained separately and then combined to produce the OCR model. Synthetic data and a smaller amount of real, human-labeled data may be used for training to increase the speed and accuracy with which the OCR text detection model and the text recognition model can be trained. After the OCR model has been trained, a workflow may be generated that includes an activity calling the OCR model, and a robot implementing the workflow may be generated and deployed.
Claims27
A computer-implemented method, comprising:
- generating a set of synthetic data, by a computing system;
- training a text recognition model for robotic process automation (RPA) on augmented human-labeled data and the set of synthetic data over a first plurality of epochs, by the computing system;
- and
- at each epoch of the first plurality of epochs, evaluating performance of the text recognition model against an evaluation dataset until a level of accuracy of the performance begins to decline, by the computing system.
A non-transitory computer-readable medium storing a computer program, the computer program configured to cause at least one processor to:
- generate a set of synthetic data;
- train a text detection model for robotic process automation (RPA) solely on the set of synthetic data over a first plurality of epochs;
- at each epoch of the first plurality of epochs, evaluate performance of the text detection model against an evaluation dataset until a level of accuracy of the performance begins to decline;
- train a text recognition model for RPA on augmented human-labeled data and the set of synthetic data over a second plurality of epochs;
- and
- at each epoch of second the plurality of epochs, evaluate performance of the text recognition model against the evaluation dataset until a level of accuracy of the performance begins to decline.
A computer-implemented method for training an optical character recognition (OCR) model, comprising:
- generating a set of synthetic data, by a computing system;
- training a text recognition model for robotic process automation (RPA) on augmented human-labeled data and the set of synthetic data over a plurality of epochs, by the computing system;
- and
- at each epoch of the plurality of epochs, evaluating performance of the text recognition model against the evaluation dataset until a level of accuracy of the performance begins to decline, by the computing system;
- combining the text detection model and the text recognition model into an optical character recognition (OCR) model, by the computing system;
- and
- training a smaller, faster OCR model using the OCR model, by the computing system.
Dependencies are inferred from the claim text.