A method for recognizing handwritten text is disclosed. The method comprises receiving data comprising a sequence of ink points; applying the received data to a neural network-based sequence classifier trained with a Connectionist Temporal Classification (CTC) output layer using forced alignment to generate an output; generating a character hypothesis as a portion of the sequence of ink points; applying the character hypothesis to a character classifier to obtain a first probability corresponding to the probability that the character hypothesis includes the given character; processing the output of the CTC output layer to determine a second probability corresponding to the probability that the given character is observed within the character hypothesis; and combining the first probability and the second probability to obtain a combined probability corresponding to the probability that the character hypothesis includes the given character.

A method is provided. The method may include, in response to electronically receiving a document, automatically classifying the document and different parts of the document, by electronically identifying a document type associated with the document and electronically tagging data associated with the different parts of the document based on classification rules. The method may further include automatically extracting the tagged data associated with the automatically classified document based on data extraction rules. The method may further include detecting first feedback associated with the classification rules and second feedback associated with the data extraction rules. The method may further include automatically generating and updating validation rules based on the identified document type, the detected first feedback, and the detected second feedback to validate the automatically classified document and the automatically tagged and extracted data.

An interaction checking method, device, and system determines health interactions, in real time, as images are collected from an environment and provides augmented informational data presentations of the same. The interactions are based on health information, historical information, and stored interaction data. In particular, the images are received from a camera viewing the environment and an identity is determined of the objects in the environment. Based on the identity determined, information about the objects is retrieved from a memory device, and an interaction warning is determined for a specific combination of the objects if the objects were to be used together. A display device augments visual data of the environment with the information about the objects in a presentation to a user that includes the interaction warning and information about the objects.

An information processing apparatus according to an embodiment of the present technology includes a classification unit and a generation unit. The classification unit classifies an object detected in a space on a basis of a predetermined criterion. The generation unit sets a priority for the object on a basis of a classification result by the classification unit, and generates position-related information regarding a position in the space on a basis of the set priority. Use of the position-related information makes it possible to improve the accuracy of autonomous movement control. This makes it possible to improve the accuracy of autonomous movement control.

In some embodiments, a system for creating a structured content object comprises a database configured to store unstructured content and a control system configured to segment the unstructured content into a plurality of elements, analyze, via a plurality of models, each of the plurality of elements, wherein each of the models is trained for a different type of content, generate, for each of the plurality of elements, confidence scores, generate, for each of the plurality of elements, bounding boxes, determine, based on the confidence scores for each of the plurality of elements, a type of content, determine a reading order, create, based on the confidence scores, the bounding boxes, and the types of content, tags including (i) the confidence scores, (ii) the bounding boxes, and (iii) the types of content for each element of the plurality of elements, and create, based on the tags, the structured content object.

A medical image processing method includes: obtaining a biological tissue image including a biological tissue, recognizing, in the biological tissue image, a first region of a lesion object in the biological tissue; recognizing a lesion attribute matching the lesion object; dividing an image region of the biological tissue in the biological tissue image into a plurality of quadrant regions; obtaining target quadrant position information of a quadrant region in which the first region is located; and generating medical service data according to the target quadrant position information and the lesion attribute.

Systems, methods, and computer-readable media are disclosed for visual labeling of training data items for training a machine learning model. Training data items may be generated for training the machine learning model. Visual labels, such as QR codes, may be created for the training data items. The creation of the training data item and the visual label may be automated. The visual labels and the training data items may be combined to obtain a labeled training data item. The labeled training data item may comprise a separator to distinguish the training data item from the visual label. The labeled training data item may be used for training and validation of the machine learning model. The machine learning model may analyze the training data item, attempt to identify the training data item, and compare the identification against the embedded label.

A method is provided. The method may include, in response to electronically receiving a document, automatically classifying the document and different parts of the document, by electronically identifying a document type associated with the document and electronically tagging data associated with the different parts of the document based on classification rules. The method may further include automatically extracting the tagged data associated with the automatically classified document based on data extraction rules. The method may further include detecting first feedback associated with the classification rules and second feedback associated with the data extraction rules. The method may further include automatically generating and updating validation rules based on the identified document type, the detected first feedback, and the detected second feedback to validate the automatically classified document and the automatically tagged and extracted data.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for model co-occurrence object detection. One of the methods includes accessing, for a training image, first data that indicates a detected bounding box for a first object depicted in the training image and a predicted type label, accessing, for the training image, ground truth data for one or more ground truth objects, determining, using the first data and the ground truth data, that i) the detected bounding box represents an object that is not a ground truth object represented by the ground truth data or ii) the predicted type label for the first object does not match a ground truth label for the first object identified by the ground truth data, determining a penalty to adjust the model using a distance between the detected bounding box and the labeled bounding box, and training the model using the penalty.

Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.