A computer-implemented method for automatically analyzing handwritten text to determine a mismatch between a purported writer and an actual writer is disclosed. The method comprises receiving two samples of digitized handwriting each allegedly created by one individual and received and entered into a digital system by another. The method further comprises performing a series of feature extractions to convert the samples into two vectors of extracted features; automatically clustering a set of vectors such that the first vector and the second vector are assigned to the same cluster among multiple clusters, based on vector similarity; and automatically determining that a same individual being associated with both the first and second samples indicates a heightened probability that the individual fraudulently created both samples. Finally, the method comprises automatically transmitting a message to flag additional samples of digitized handwriting entered into a digital system as possibly fraudulent.

According to embodiments of the present invention, a method and system for generating a training dataset for keypoint detection are provided. The system includes an optical marker-based motion capture system to capture markers as 3D trajectories; and video cameras to simultaneously capture sequences of 2D images. Each marker is placed on a bone landmark or keypoint of a subject. The method, performed by a computer in the system, includes projecting each trajectory to each image to determine a 2D location for each marker; interpolating a 3D position therefrom; generating a bounding box around the subject; and generating the training dataset including at least one image, and the determined 2D location of each marker and the bounding box therein. According to further embodiments, a method and system for predicting 3D locations of virtual markers on a marker-less subject using a neural network trained by the generated training dataset are also provided.

A document creation support apparatus comprising at least one processor, wherein the processor is configured to: acquire an image and a character string related to the image; extract at least one feature region included in the image; specify a specific region that is a region corresponding to a phrase included in the character string, in the feature region; and present information for supporting creation of a document including the character string based on a result of the specifying.

Computer program products, methods, systems, apparatus, and computing entities provide a unique single-shot text detector that generates word-level text bounding boxes in an image by at least identifying text regions in the image via an automatically learned attention map and by conducting pixel-wise review of text; aggregating multi-scale inception features; generating, based at least in part on the multi-scale inception features, a set of aggregated inception features; and generating, using at least the set of aggregated inception features, the word-level text bounding boxes in the image.

A system and method for training a neural network is implemented for detecting at least one entity in a document to derive relevant inferences therefrom. The method describes obtaining at least one document. The at least one document is processed, via a detection module, to detect a widget entity. The detected widget entity is classified as active or inactive based on a detected state of the widget entity. The classified widget entity is modified into a corresponding machine-readable widget-entity based on the detected state. The at least one document is processed, via an extraction module, to detect a text entity in near vicinity of the classified widget entity. A training pair comprising the machine-readable widget entity and the corresponding text entity is generated. The neural network is trained using the generated training pair.

During an image-analysis technique, the system calculates features by performing image analysis (such as optical character recognition) on a received image of a document. Using these features, as well as spatial and grammatical constraints, the system determines a layout of the document. For example, the layout may be determined using constraint-based optimization based on the spatial and the grammatical constraints. Note that the layout specifies locations of content in the document, and may be used to subsequently extract the content from the image and/or to allow a user to provide feedback on the extracted content by presenting the extracted content to the user in a context (i.e., the determined layout) that is familiar to the user.

An apparatus obtains first transformation information, such as a first transformation matrix, to be used for coordinate transformation between a coordinate system in an overall image prepared beforehand and a coordinate system in a first captured image, by comparing a feature point extracted from the overall image and a feature point extracted from the first captured image. In a case where the first transformation information is updated, the apparatus generates a partial image from the overall image based on an image-taking position of a just preceding image, and compares a feature point extracted from the partial image with a feature point extracted from a captured image to be used for updating of the first transformation information, and accordingly obtains transformation information for updating. The apparatus updates the first transformation information by using the obtained transformation information for updating. Thus, accuracy of tracking processing is improved.

A method of determining macrotexture of an object is disclosed which includes obtaining a plurality of stereo images from an object by an imaging device, generating a coordinate system for each image of the plurality of stereo images, detecting one or more keypoints each having a coordinate in each image of the plurality of stereo images, wherein the coordinate system is based on a plurality of ground control points (GCPs) with apriori position knowledge of each of the plurality of GCPs, generating a sparse point cloud based on the one or more keypoints, reconstructing a 3D dense point cloud of the object based on the generated sparse point cloud and based on neighboring pixels of each of the one or more keypoints and calculating the coordinates of each pixel of the 3D dense point cloud, and obtaining the macrotexture based on the reconstructed 3D dense point cloud of the object.

During an image-analysis technique, the system calculates features by performing image analysis (such as optical character recognition) on a received image of a document. Using these features, as well as spatial and grammatical constraints, the system determines a layout of the document. For example, the layout may be determined using constraint-based optimization based on the spatial and the grammatical constraints. Note that the layout specifies locations of content in the document, and may be used to subsequently extract the content from the image and/or to allow a user to provide feedback on the extracted content by presenting the extracted content to the user in a context (i.e., the determined layout) that is familiar to the user.

A method of customized installation of access control hardware according to one embodiment includes capturing, by a camera of a mobile device, at least one image of an installation location for the access control hardware, generating a set of customized installation instructions for the access control hardware at the installation location based on the at least one image, and displaying the customized installation instructions on a graphical user interface of the mobile device.