Abstract: Methods, systems, and computer program products for vision-based cell structure recognition using hierarchical neural networks and cell boundaries to structure clustering are provided herein. A computer-implemented method includes detecting a style of the given table using at least one style classification model; selecting, based at least in part on the detected style, a cell detection model appropriate for the detected style; detecting cells within the given table using the selected cell detection model; and outputting, to at least one user, information pertaining to the detected cells comprising image coordinates of one or more bounding boxes associated with the detected cells.

Abstract: Methods, systems, and computer program products for cooperative neural networks with spatial containment constraints are provided herein. A computer-implemented method includes dividing a processing task into multiple sub-tasks; training multiple independent neural networks, such that at least some of the multiple sub-tasks correspond to different ones of the multiple independent neural networks; defining, via implementing constraint-based domain knowledge related to the processing task in connection with the multiple independent neural networks, a constraint loss for a given one of the multiple sub-tasks, the constraint loss being dependent on output from at least one of the other multiple sub-tasks; and effecting re-training of at least a portion of the multiple independent neural networks by incorporating the constraint loss into at least one of the multiple independent neural networks.

Abstract: Methods, systems, and computer program products for vision-based cell structure recognition using hierarchical neural networks and cell boundaries to structure clustering are provided herein. A computer-implemented method includes detecting a style of the given table using at least one style classification model; selecting, based at least in part on the detected style, a cell detection model appropriate for the detected style; detecting cells within the given table using the selected cell detection model; and outputting, to at least one user, information pertaining to the detected cells comprising image coordinates of one or more bounding boxes associated with the detected cells.

Abstract: Methods, systems, and computer program products for vision-based cell structure recognition using hierarchical neural networks and cell boundaries to structure clustering are provided herein. A computer-implemented method includes detecting a style of the given table using at least one style classification model; selecting, based at least in part on the detected style, a cell detection model appropriate for the detected style; detecting cells within the given table using the selected cell detection model; and outputting, to at least one user, information pertaining to the detected cells comprising image coordinates of one or more bounding boxes associated with the detected cells.

Abstract: Methods, systems, and computer program products for vision-based cell structure recognition using hierarchical neural networks and cell boundaries to structure clustering are provided herein. A computer-implemented method includes detecting a style of the given table using at least one style classification model; selecting, based at least in part on the detected style, a cell detection model appropriate for the detected style; detecting cells within the given table using the selected cell detection model; and outputting, to at least one user, information pertaining to the detected cells comprising image coordinates of one or more bounding boxes associated with the detected cells.

Abstract: Methods, systems, and computer program products for cooperative neural networks with spatial containment constraints are provided herein. A computer-implemented method includes dividing a processing task into multiple sub-tasks; training multiple independent neural networks, such that at least some of the multiple sub-tasks correspond to different ones of the multiple independent neural networks; defining, based at least in part on constraint-based domain knowledge related to the processing task, at least one constraint loss for a given one of the multiple sub-tasks, the at least one constraint loss being dependent on output from at least one of the other multiple sub-tasks; and re-training at least a portion of the multiple independent neural networks, the re-training being dependent on using the at least one constraint loss.