Abstract: Interfaces and systems are provided for harvesting ground truth from forms to be used in training models based on key-value pairings in the forms and to later use the trained models to identify related key-value pairings in new forms. Initially, forms are identified and clustered to identify a subset of forms to label with the key-value pairings. Users provide input to identify keys to use in labeling and then select/highlight text from forms that are presented concurrently with the keys in order to associate the highlighted text with the key(s) as the corresponding key-value pairing(s). After labeling the forms with the key-value pairings, the key-value pairing data is used as ground truth for training a model to independently identify the key-value pairing(s) in new forms. Once trained, the model is used to identify the key-value pairing(s) in new forms.

Abstract: The disclosed technology is generally directed to optical character recognition for forms. In one example of the technology, optical character recognition is performed on a plurality of forms. The forms of the plurality of forms include at least one type of form. Anchors are determined for the forms, including corresponding anchors for each type of form of the plurality of forms. Feature rules are determined, including corresponding feature rules for each type of form of the plurality of forms. Features and labels are determined for each form of the plurality of forms. A training model is generated based on a ground truth that includes a plurality of key-value pairs corresponding to the plurality of forms, and further based on the determined features and labels for the plurality of forms.

Abstract: The disclosed technology is generally directed to optical text recognition for forms. In one example of the technology, line grouping rules are generated based on the generic forms and a ground truth for the generic forms. Line groupings are applied to the generic forms based on the line grouping rules. Feature extraction rules are generated. Features are extracted from the generic forms based on the feature extraction rules. A key-value classifier model is generated, such that the key-value classifier model is configured to determine, for each line of a form: a probability that the line is a value, and a probability that the line is a key. A key-value pairing model is generated, such that the key-value pairing model is configured to predict, for each key in a form, which value in the form corresponds to the key.

Abstract: Interfaces and systems are provided for harvesting ground truth from forms to be used in training models based on key-value pairings in the forms and to later use the trained models to identify related key-value pairings in new forms. Initially, forms are identified and clustered to identify a subset of forms to label with the key-value pairings. Users provide input to identify keys to use in labeling and then select/highlight text from forms that are presented concurrently with the keys in order to associate the highlighted text with the key(s) as the corresponding key-value pairing(s). After labeling the forms with the key-value pairings, the key-value pairing data is used as ground truth for training a model to independently identify the key-value pairing(s) in new forms. Once trained, the model is used to identify the key-value pairing(s) in new forms.

Abstract: The disclosed technology is generally directed to optical text recognition for forms. In one example of the technology, line grouping rules are generated based on the generic forms and a ground truth for the generic forms. Line groupings are applied to the generic forms based on the line grouping rules. Feature extraction rules are generated. Features are extracted from the generic forms based on the feature extraction rules. A key-value classifier model is generated, such that the key-value classifier model is configured to determine, for each line of a form: a probability that the line is a value, and a probability that the line is a key. A key-value pairing model is generated, such that the key-value pairing model is configured to predict, for each key in a form, which value in the form corresponds to the key.

Abstract: The disclosed technology is generally directed to optical character recognition for forms. In one example of the technology, optical character recognition is performed on a plurality of forms. The forms of the plurality of forms include at least one type of form. Anchors are determined for the forms, including corresponding anchors for each type of form of the plurality of forms. Feature rules are determined, including corresponding feature rules for each type of form of the plurality of forms. Features and labels are determined for each form of the plurality of forms. A training model is generated based on a ground truth that includes a plurality of key-value pairs corresponding to the plurality of forms, and further based on the determined features and labels for the plurality of forms.

Abstract: The present invention relates to a liver-caring medicine that cures alcohol-induced liver cancer and contains active ingredients from the fruiting body and the mycelium of Antrodia Camphorata or Antrodia Cinnamomea, which is a kind of mushroom that only grows inside a unique plant in Taiwan, a Cinnamomum kanehirae tree.