Automation Anywhere, Inc.

Lincoln Global, Inc.

Yogesh Savalia

Nakuldev Patel

Derek S Chan

Mukesh Methaniya

Priti Sundar Hazra

Christopher J Williams

Bryan K White

Dan Dailey

Andrew Oxford

Information contained in tables in a digitized document is extracted by retrieving table layout data regarding bounding boxes, each being auto-generated by the system and/or (re)generated by a user to the digitized image of a sample document. A row template is used to identify a first table, by automatically scanning within the document. Upon detecting a possible row in the input image, a Row Possibility Confidence Value (RPCV) is generated that indicates a likelihood that the possible row corresponds to an actual row in the first table. The possible row is regarded as an actual row if the RPCV exceeds a predetermined threshold value. For repeated tables in a document only the first table needs to be identified via bounding boxes. Also, related tables can be linked to permit linked data to be extracted to a structured file. Also, only the primary column in a readable and existent table header is required to extract table values across columns.

Computerized recognition and extraction of tables in digitized documents

A system that automatically recognizes checkboxes within a document and extracts the contents thereof. A user views a digitized document and annotates the document by identifying checkboxes contained in the document, by way of visually perceptible bounding boxes. The annotated document is processed by a machine learning engine that employs multiple convolutional operations followed by a global average pooling layer, a fully connected layer with 1024 node and 'ReLU' activation, a fully connected layer with 2 node and 'softmax' activation. The identified checkboxes and their contents are stored as label-value pairs, where the label identifies the checkbox and the value identifies the value of the checkbox, which can be either Yes, No, or No checkbox found.

Computerized recognition of checkboxes in digitized documents

A system for remotely positioning an end effector includes a sensor in an input device aligned with an axis to generate a signal reflective of an angular displacement of the sensor about the axis. A processor receives the signal and executes a set of logic stored in a memory to filter the signal, smooth the signal, and generate a control signal to the end effector that is proportional to the angular displacement of the sensor about the axis. A method for remotely positioning an end effector includes moving an input device, sensing an angular displacement of a sensor from an axis, generating a signal reflective of the angular displacement of the sensor about the axis, smoothing the signal, and generating a control signal to the end effector that is proportional to the angular displacement of the sensor about the axis.

System and method for remotely positioning an end effector

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Vadodara, India

Yogesh Savalia