Oracle International Corporation

Pccw Vuclip (Singapore) Pte. Ltd.

Samsung Electronics Co., Ltd.

Kulbhushan Pachauri

Amit Agarwal

Jun Qian

Amith Dsouza

Iman Zadeh

Aishwarya Aishwarya

A computing device may access visually rich documents comprising an image and metadata. A graph, based on the image or metadata, can be generated for a visually rich document. The graph's nodes can correspond to words from the visually rich document. Features for nodes can be determined by the device. The device may generate model labeled graphs by assigning a pseudo-label to nodes using a pretrained model. The device may generate a plurality of graph labeled graphs by assigning a pseudo-label to nodes by matching a first node from a first graph to at least a second node from a second graph. The device may generate a plurality of updated graphs by cross referencing labels from the model labeled graphs and the graph labeled graphs. Until a change in labels is below a threshold, a model can be trained to perform key-value extraction using the updated graphs.

Pseudo labelling for key-value extraction from documents

A computing device may receive a set of user documents. Data may be extracted from the documents to generate a first graph data structure with one or more initial graphs containing key-value pairs. A model may be trained on the first graph data structure to classify the pairs. Until a set of evaluation metrics for the model exceeds a set of deployment thresholds: generating, a set of evaluation metrics may be generated for the model. The set of evaluation metrics may be compared to the set of deployment thresholds. In response to a determination that the set of evaluation metrics are below the set of deployment thresholds: one or more new graphs may be generated from the one or more initial graphs in the first graph data structure to produce a second graph data structure. The first and second graph can be used to train the model.

Techniques for graph data structure augmentation

The present disclosure proposes an image processing method according to an embodiment. The image processing method includes the operations of obtaining a color component value from an input image, determining a dynamic range of the obtained color component value, determining a scaling factor for converting the dynamic range of the color component value, based on the determined dynamic range of the color component value and a permissible range of the color component value, and scaling the color component value, based on the determined scaling factor.

Method for processing image using dynamic range of color component, and device therefor

Approaches, techniques, and mechanisms are disclosed for generating thumbnails. According to one embodiment, a subset of images each depicting character face(s) is identified from a collection of images. An unsupervised learning method is applied to automatically cluster the subset of images into image clusters. Top image clusters are selected from the image clusters based at least in part on weighted scores of images clustered within the image clusters. Thumbnail(s) are generated from images in the top image clusters.

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Bengaluru, India

Kulbhushan Pachauri