Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media a configurable handwriting-recognition framework that can be constructed according to one or more aspects of a digital image that depicts textual content. The disclosed systems can insert model socket-specific models into model sockets of the configurable handwriting-recognition framework to perform distinct functions to generate a genealogical record from a handwriting image. Further, the disclosed systems can use model-socket adapters to standardize outputs of the model socket-specific models to create a uniform workflow. In addition, the disclosed systems can modify a genealogical database according to the genealogical record.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for processing user-generated genealogical content. In particular, in one or more embodiments, the disclosed systems receive digital images reflecting user-generated genealogical-content items and utilize a content-classification model to generate record classifications and route the digital images to one or more specialized processing pipelines to extract genealogical data from the digital images. In some embodiments, the disclosed systems use a pipeline comprised of an ensemble of models, including language-specific and document-specific models, to extract genealogical data based on the record classification. In other embodiments, the disclosed systems use a large language model to extract genealogical data based on the record classification. Further, in one or more embodiments, the disclosed systems generate genealogical records using the extracted genealogical data.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating bounding boxes for an image in accordance with one or more embodiments. The disclosed systems can generate a set of hidden states for an image of a document. The disclosed systems can generate a set of bounding boxes and a set of tokens from the hidden states utilizing a first head component and a second head component of a record generation model. The disclosed systems can aggregate a plurality of bounding boxes into a bounding box group. Additionally, the disclosed systems can provide the image depicting the bounding box group for display within a graphical user interface of a client device. Additionally or alternatively, the disclosed systems can train the record generation model, in part, by determining that a loss associated with masking one or more training bounding boxes satisfies a threshold loss.

Abstract: Systems and methods detect cortical arousal events from a single time-varying ECG signal that is obtained via single-lead ECG. A pre-trained deep neural network transforms the ECG signal into a sequence of cortical-arousal probabilities. The deep neural network includes an inception module, a residual neural network, and a long short-term memory neural network to identify structure in the ECG signal that distinguishes periods of cortical arousal from periods without cortical arousal.

Abstract: Disclosed herein relates to example embodiments for recognizing handwritten information in a genealogical record. A computing server may receive a genealogical record. The genealogical record may take the form of an image of a physical form having a structured layout, fields, and handwritten information. The computing server may divide the genealogical record into a plurality of areas based on the structured layout. The computing server may identify, for a particular area, a type of field that is included within the particular area. The computing server may select a handwriting recognition model for identifying the handwritten information in the particular area. The handwriting recognition model may be selected based on the type of the field. The computing server may input an image of the particular area to the handwriting recognition model to generate text of the handwritten information. The computing server may store the text of the handwritten information.

Abstract: Disclosed herein relates to example embodiments for recognizing handwritten information in a genealogical record. A computing server may receive a genealogical record. The genealogical record may take the form of an image of a physical form having a structured layout, fields, and handwritten information. The computing server may divide the genealogical record into a plurality of areas based on the structured layout. The computing server may identify, for a particular area, a type of field that is included within the particular area. The computing server may select a handwriting recognition model for identifying the handwritten information in the particular area. The handwriting recognition model may be selected based on the type of the field. The computing server may input an image of the particular area to the handwriting recognition model to generate text of the handwritten information. The computing server may store the text of the handwritten information.

Abstract: Disclosed herein relates to example embodiments for recognizing handwritten information in a genealogical record. A computing server may receive a genealogical record. The genealogical record may take the form of an image of a physical form having a structured layout, fields, and handwritten information. The computing server may divide the genealogical record into a plurality of areas based on the structured layout. The computing server may identify, for a particular area, a type of field that is included within the particular area. The computing server may select a handwriting recognition model for identifying the handwritten information in the particular area. The handwriting recognition model may be selected based on the type of the field. The computing server may input an image of the particular area to the handwriting recognition model to generate text of the handwritten information. The computing server may store the text of the handwritten information.

Abstract: Systems and methods detect cortical arousal events from a single time-varying ECG signal that is obtained via single-lead ECG. A pre-trained deep neural network transforms the ECG signal into a sequence of cortical-arousal probabilities. The deep neural network includes an inception module, a residual neural network, and a long short-term memory neural network to identify structure in the ECG signal that distinguishes periods of cortical arousal from periods without cortical arousal.