Abstract: Dynamic inference is leveraged to provide online sequence data labeling. This provides real-time alternatives to current methods of inference for sequence data. Instances estimate an amount of uncertainty in a prediction of labels of sequence data and then dynamically predict a label when an uncertainty in the prediction is deemed acceptable. The techniques utilized to determine when the label can be generated are tunable and can be personalized for a given user and/or a system. Employed decoding techniques can be dynamically adjusted to tradeoff system resources for accuracy. This allows for fine tuning of a system based on available system resources. Instances also allow for online inference because the inference does not require knowledge of a complete set of sequence data.

Abstract: A computer-implemented method and apparatus are provided for populating an electronic form from an electronic image. The method and apparatus identify a size, orientation and position of an object within the electronic image, and identify information elements from pixels within the image that correspond to the object. Fields of the electronic form are displayed to a user along with the identified information elements through a graphical user interface. The information elements are parsed into tagged groups of different information types. At least some of the fields of the electronic form are populated with the tagged groups to produce a populated form. The user is allowed to edit the populated fields through the graphical user interface.

Abstract: The present invention relates to a method of automatically processing an electronic document for routing over a computer network. The method includes recognizing text in the document to identify a candidate address, accessing a collection of potential destinations and comparing the candidate address to the collection of potential destinations to determine a destination for the document.

Abstract: A method detects a moving object in a temporal sequence of images. Images are selected from the temporally ordered sequence of images. A set of functions is applied to the selected images to generate a set of combined images. A linear combination of filters is applied to a detection window in the set of combined images to determine motion and appearance features of the detection window. The motion and appearance features are summed to determine a cumulative score, which enables a classification of the detection window as including the moving object.

Abstract: A method for detects a specific object in an image. An orientation of an arbitrary object with respect to an image plane is determined and one of a plurality orientation and object specific classifiers is selected according to the orientation. The arbitrary object is classified as a specific object with the selected orientation and object specific classifier.

Abstract: A two-dimensional representation of a document is leveraged to extract a hierarchical structure that facilitates recognition of the document. The visual structure is grammatically parsed utilizing two-dimensional adaptations of statistical parsing algorithms. This allows recognition of layout structures (e.g., columns, authors, titles, footnotes, etc.) and the like such that structural components of the document can be accurately interpreted. Additional techniques can also be employed to facilitate document layout recognition. For example, grammatical parsing techniques that utilize machine learning, parse scoring based on image representations, boosting techniques, and/or “fast features” and the like can be employed to facilitate in document recognition.

Abstract: Image recognition is utilized to facilitate in scoring parse trees for two-dimensional recognition tasks. Trees and subtrees are rendered as images and then utilized to determine parsing scores. Other instances of the subject invention can incorporate additional features such as stroke curvature and/or nearby white space as rendered images as well. Geometric constraints can also be employed to increase performance of a parsing process, substantially improving parsing speed, some even resolvable in polynomial time. Additional performance enhancements can be achieved in yet other instances of the subject invention by employing constellations of integral images and/or integral images of document features.

Abstract: A computer-implemented word processing system comprises an interface component that receives a features vector associated with an electronic document. An analysis component communicatively coupled to the interface component analyzes the features vector and determines a viewing mode in which to display the electronic document. In accordance with one aspect of the subject invention, the viewing mode can be one of a conventional viewing mode and a viewing mode associated with enhanced readability.

Abstract: Grammatical parsing is utilized to parse structured layouts that are modeled as grammars. This type of parsing provides an optimal parse tree for the structured layout based on a grammatical cost function associated with a global search. Machine learning techniques facilitate in discriminatively selecting features and setting parameters in the grammatical parsing process. In one instance, labeled examples are parsed and a chart is generated. The chart is then converted into a subsequent set of labeled learning examples. Classifiers are then trained utilizing conventional machine learning and the subsequent example set. The classifiers are then employed to facilitate scoring of succedent sub-parses. A global reference grammar can also be established to facilitate in completing varying tasks without requiring additional grammar learning, substantially increasing the efficiency of the structured layout analysis techniques.

Abstract: A discriminative grammar framework utilizing a machine learning algorithm is employed to facilitate in learning scoring functions for parsing of unstructured information. The framework includes a discriminative context free grammar that is trained based on features of an example input. The flexibility of the framework allows information features and/or features output by arbitrary processes to be utilized as the example input as well. Myopic inside scoring is circumvented in the parsing process because contextual information is utilized to facilitate scoring function training.

Abstract: The subject invention relates to facilitating text detection. The invention employs a boosted classifier and a transductive classifier to provide accurate and efficient text detection systems and/or methods. The boosted classifier is trained through features generated from a set of training connected components and labels. The boosted classifier utilizes the features to classify the training connected components, wherein inferred labels are conveyed to a transductive classifier, which generates additional properties. The initial set of features and the properties are utilized to train the transductive classifier. Upon training, the system and/or methods can be utilized to detect text in data under text detection, wherein unlabeled data is received, and connected components are extracted therefrom and utilized to generate corresponding feature vectors, which are employed to classify the connected components using the initial boosted classifier.

Abstract: An object detection system for detecting instances of an object in a digital image includes an image integrator and an object detector, which includes a classifier (classification function) and image scanner. The image integrator receives an input image and calculates an integral image representation of the input image. The image scanner scans the image in same sized subwindows. The object detector uses a cascade of homogenous classification functions or classifiers to classify the subwindows as to whether each subwindow is likely to contain an instance of the object. Each classifier evaluates one or more features of the object to determine the presence of such features in a subwindow that would indicate the likelihood of an instance of the object in the subwindow.

Abstract: A method for efficiently comparing two trinary logic representations, including the steps of creating a first data structure (a VALUE data structure) representative of a first set of properties; creating a second data structure (a KNOWN data structure) representative of whether the first set of properties is known; creating a third data structure (a TARGET data structure) representative of a target set of properties; creating a fourth data structure (a WANT data structure) representative of whether the target set of properties is wanted; and comparing the first, second, third, and fourth data structures using bit-wise binary operations to determine whether the first set of known properties are wanted as a target set of properties. In exemplary embodiments, the bit-wise binary operations are performed according to the Boolean equation: (not WANT) or (KNOWN and ((TARGET xor VALUE))).

Abstract: A device for monitoring movement of an object is provided. A first module is configured to secure to the object. A second module, capable of electrically connecting to the first module, includes at least a rechargeable battery and a memory capable of storing a history of movement data. A third module, capable of electrically connecting with the second module, includes a data modem capable of connecting to a remote station, and a battery charger. When the second module is connected to the first module, the memory periodically records available location data representing a position of the device at the time of recording. When the second module is connected to the third module, the memory downloads through the data modem and the battery charger charges the battery.

Abstract: A system and method for determining hierarchical information is described. Aspects include using the Collins model for parsing non-textual information into hierarchical content. The system and process assign labels to lines that indicate how the lines relate to one another.

Abstract: A system and method for determining hierarchical information is described. Aspects include using the Collins model for parsing non-textual information into hierarchical content. The system and process assign labels to lines that indicate how the lines relate to one another.

Abstract: A method recognizes an object in an image. Gallery images include identified objects, and a probe image includes an unidentified object. The probe image is paired with each gallery image to generate pairs of images. First filters are applied to the probe image of each pair to obtain a first feature value for each application of each filter. Second filters are applied to the each gallery image of each pair to obtain a second feature value for each application of each filter. The feature values are summed for each application of each filter, and for each application, a score is set to an acceptance weight if the sum is greater than a predetermined threshold, and otherwise setting the score to a rejection weight. The scores are summed for all of the application, and the probe image is identified with any gallery image when the score is greater than zero.

Abstract: A method detects an object, such a face, in an image. The image is first partitioned into patches of various sizes using either an integral image or a Gaussian pyramid. Features in each patch are evaluated to determine a cumulative score. The evaluating is repeated while the cumulative score is within a range of an acceptance threshold and a rejection threshold, and otherwise the image is rejected when the accumulated score is less than the rejection threshold and accepted as including the object when the cumulative score is greater than the acceptance threshold.

Abstract: The present invention leverages spatial relationships to provide a systematic means to recognize text and/or graphics. This allows augmentation of a sketched shape with its symbolic meaning, enabling numerous features including smart editing, beautification, and interactive simulation of visual languages. The spatial recognition method obtains a search-based optimization over a large space of possible groupings from simultaneously grouped and recognized sketched shapes. The optimization utilizes a classifier that assigns a class label to a collection of strokes. The overall grouping optimization assumes the properties of the classifier so that if the classifier is scale and rotation invariant the optimization will be as well. Instances of the present invention employ a variant of AdaBoost to facilitate in recognizing/classifying symbols. Instances of the present invention employ dynamic programming and/or A-star search to perform optimization.

Abstract: A device for monitoring movement of an object is provided. A first module is configured to secure to the object. A second module, capable of electrically connecting to the first module, includes at least a rechargeable battery and a memory capable of storing a history of movement data. A third module, capable of electrically connecting with the second module, includes a data modem capable of connecting to a remote station, and a battery charger. When the second module is connected to the first module, the memory periodically records available location data representing a position of the device at the time of recording. When the second module is connected to the third module, the memory downloads through the data modem and the battery charger charges the battery.