Ancestry.com Operations Inc.

Ancestry.com Dna, LLC

Jack Reese

Shawn Reid

Laryn Brown

Michael Murdock

Azadeh Moghtaderi

Yen-Yun Yu

Mohammad K Ebrahimpour

Jiayun Li

Tyler Folkman

Peng Jiang

Gopalkrishna Balkrishna Veni

Ruhan Wang

Tsung-Nan Liu

Randy Hansen

Mark Oswald

Yingrui Yang

Ahna R Girshick

Chris Brookhart

Luong Ruiz

Randon Morford

Christopher Brookhart

Michael Benjamin Brodie

Simon Chu

Luca Lugini

Methods, systems, and computer-program products for image enhancement include receiving an image and optionally a user request, classify the image, crop image components of the image, restore cropped image components of the image, colorized restored image components, and reconstruct the image from the colorized, restored image components and other components. The other components may include text components that are restored in a separate treatment pipeline.

Image enhancement in a genealogy system

Systems and methods for importing documents are described. An input image is received and preprocessed. OCR and/or page segmentation and chapter detection are performed. Special-case processing is performed for lists, tables, free text, and other categories. Anaphora analysis, stemming, lemmatization, and relationship detection are performed. A genealogical tree is generated, augmented, or merged based on the extracted entities and relationships.

Machine-learning based automated document integration into genealogical trees

Systems, methods, and/or computer-program products for generating a photo composite are configured to identify a facial photo, assign a community such as a genetic community to the identified facial photo, select and retrieve a subset of photos from a genetic community, determine a landmark in each photo of the subset of photos, compute and apply a similarity transform to each photo based and its landmarks, update estimates of transformed averaged landmarks, calculate a triangularization of the transformed averaged landmarks, warp images to the average image landmarks, and combine the intensities of the warped images. The communities are identified from genetic information. The facial photos are identified from a network of genealogical trees and/or a collection of historical records.

Photo composites

Systems and methods for classifying historical images. A feature extractor may create feature vectors corresponding to a plurality of images. A first classification of the plurality of images may be performed based on the plurality of feature vectors, which may include assigning a label to each of the plurality of images and assigning a probability for each of the assigned labels. The assigned probability for each of the assigned labels may be related to a statistical confidence that a particular assigned label is correctly assigned to a particular image. A subset of the plurality of images may be displayed to a display device. An input corresponding to replacement of an incorrect label with a corrected label for a certain image may be received from a user. A second classification of the plurality of images based on the input from the user may be performed.

Clustering historical images using a convolutional neural net and labeled data bootstrapping

Systems and methods for training a machine learning (ML) ranking model to rank genealogy hints are described herein. One method includes retrieving a plurality of genealogy hints for a target person, where each of the plurality of genealogy hints corresponds to a genealogy item and has a hint type of a plurality of hint types. The method includes generating, for each of the plurality of genealogy hints, a feature vector having a plurality of feature values, the feature vector being included in a plurality of feature vectors. The method includes extending each of the plurality of feature vectors by at least one additional feature value based on the number of features of one or more other hint types of the plurality of hint types. The method includes training the ML ranking model using the extended plurality of feature vectors and user-provided labels.

Genealogy item ranking and recommendation

Embodiments described herein relate generally to a methodology of efficient object classification within a visual medium. The methodology utilizes a first neural network to perform an attention based object localization within a visual medium to generate a visual mask. The visual mask is applied to the visual medium to generate a masked visual medium. The masked visual medium may be then fed into a second neural network to detect and classify objects within the visual medium.

Ventral-dorsal neural networks: object detection via selective attention

Systems and methods for registering documents. A two-dimensional document image and one or more form images may be obtained. The document image may be projected onto a horizontal axis and a vertical axis to create a horizontal document projection and a vertical document projection. The form images may be projected onto the horizontal axis and the vertical axis to create a horizontal form projection and a vertical form projection. The horizontal document projection may be correlated with the horizontal form projection of the form images and the vertical document projection may be correlated with the vertical form projection of the form images. Correlation scores may be calculated based on the correlations.

2D multi-coordinate adaptive dynamics programming method for robust document registration

A handwriting recognition system converts word images on documents, such as document images of historical records, into computer searchable text. Word images (snippets) on the document are located, and have multiple word features identified. For each word image, a word feature vector is created representing multiple word features. Based on the similarity of word features (e.g., the distance between feature vectors), similar words are grouped together in clusters, and a centroid that has features most representative of words in the cluster is selected. A digitized text word is selected for each cluster based on review of a centroid in the cluster, and is assigned to all words in that cluster and is used as computer searchable text for those word images where they appear in documents. An analyst may review clusters to permit refinement of the parameters used for grouping words in clusters, including the adjustment of weights and other factors used for determining the distance between feature vectors.

System and method for transcribing handwritten records using word grouping with assigned centroids

System and method for transcribing historical records into digitized text

System and method for transcribing handwritten records using word groupings based on feature vectors

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Lindon, UT, United States of America

Jack Reese