Abstract: In accordance with one aspect of the present invention, disclosed is an image analysis and conversion method and system, where digital ink images are converted to structured object representations of the digital ink images, capable of being edited by a structured text/graphics editor.

Abstract: A method is provided for parsing a document having a plurality of lines on which items are listed spanning one or more lines. It includes: obtaining a plurality of candidates, representing hypothetical items within the document, each candidate spanning one or more lines and having a local cost representing a confidence in a quality of the candidate compared to a model; determining labeling costs for intervals of the document defined between pairs of lines, each interval containing candidates therein, each labeling cost reflecting a configuration of the candidates within the interval; identifying a best labeling for each interval based on the labeling costs determined for that interval, the best labeling corresponding to one of the configurations of the candidates within the interval; defining a global objective function; and selecting a subset of the candidates such that the global objective function is optimized, based on the identified best labelings.

Abstract: A system and method classify a test image. At least one processor receives a data graph computed from the test image. Further, a graph lattice is received. The graph lattice includes a plurality of nodes, each including a subgraph, a weight and at least one mapping of the subgraph to data graphs of a plurality of training images. The training images correspond to a plurality of classes. The data graph of the test image is mapped by the subgraphs of the nodes. Mappings between the graph lattice and the data graphs of the training images are compared with mappings between the graph lattice and the data graph of the test image to determine weighted votes of similarity between the data graphs of the training images and the data graph of the test image. The class of the test image is determined from the weighted votes.

Abstract: A system and method to classify forms. An image representing a form of an unknown document type is received. The image includes line-art. Further, a plurality of template models corresponding to a plurality of different document types is received. The plurality of different document types is intended to include the correct document type of the unknown document. A subset of the plurality of template models are selected as candidate template models. The candidate template models include line-art junctions best matching line-art junctions of the received image. One of the candidate template models is selected as a best candidate template model. The best candidate template model includes horizontal and vertical lines best matching horizontal and vertical lines of the received image, respectively, aligned to the best candidate template model.

Abstract: A method and system to localize data fields of a form. An image of a form is received, where the form includes data fields. Word boxes of the image are identified. The word boxes are grouped into candidate zones, where each of the candidate zones includes one or more of the word boxes. Hypotheses are formed from the data fields and the candidate zones, where each hypothesis assigns one of the candidate zones to one of the data fields or a null data field. A constrained optimization search of the hypotheses is performed for an optimal set of hypotheses. The optimal set of hypotheses assigns word box groups to corresponding data fields.

Abstract: Methods and systems of the present embodiment provide segmenting of connected components of markings found in document images. Segmenting includes detecting aligned text. From this detected material an aligned text mask is generated and used in processing of the images. The processing includes breaking connected components in the document images into smaller pieces or fragments by detecting and segregating the connected components and fragments thereof likely to belong to aligned text.

Abstract: A document recognition system and method, where images are represented as a collection of primitive features whose spatial relations are represented as a graph. Useful subsets of all the possible subgraphs representing different portions of images are represented over a corpus of many images. The data structure is a lattice of subgraphs, and algorithms are provided means to build and use the graph lattice efficiently and effectively.

Abstract: Methods and systems of the present embodiment provide segmenting of connected components of markings found in document images. Segmenting includes detecting aligned text. From this detected material an aligned text mask is generated and used in processing of the images. The processing includes breaking connected components in the document images into smaller pieces or fragments by detecting and segregating the connected components and fragments thereof likely to belong to aligned text.

Abstract: Methods and system employing the same for finding repeated structure for data extraction from document images are provided. A reference record and one or more reference fields thereof are identified from a document image. One or more candidate fields are generated for each of the reference fields. One or more best candidate records from the candidate fields are selected using a probabilistic model and an optimal record set is determined from the best candidate records.

Abstract: A method and system generates an idealized image of a form. An image of a form and a template model of the form are received. The form includes data fields. Word boxes of the image are identified. The word boxes are assigned to corresponding data fields of the form. An idealized image of the from is generated based on the assignments and the template model.

Abstract: A system and method to classify forms. An image representing a form of an unknown document type is received. The image includes line-art. Further, a plurality of template models corresponding to a plurality of different document types is received. The plurality of different document types is intended to include the correct document type of the unknown document. A subset of the plurality of template models are selected as candidate template models. The candidate template models include line-art junctions best matching line-art junctions of the received image. One of the candidate template models is selected as a best candidate template model. The best candidate template model includes horizontal and vertical lines best matching horizontal and vertical lines of the received image, respectively, aligned to the best candidate template model.

Abstract: A method and system to localize data fields of a form. An image of a form is received, where the form includes data fields. Word boxes of the image are identified. The word boxes are grouped into candidate zones, where each of the candidate zones includes one or more of the word boxes. Hypotheses are formed from the data fields and the candidate zones, where each hypothesis assigns one of the candidate zones to one of the data fields or a null data field. A constrained optimization search of the hypotheses is performed for an optimal set of hypotheses. The optimal set of hypotheses assigns word box groups to corresponding data fields.

Abstract: A system and method generate a graph lattice from exemplary images. At least one processor receives exemplary data graphs of the exemplary images and generates graph lattice nodes of size one from primitives. Until a termination condition is met, the at least one processor repeatedly: 1) generates candidate graph lattice nodes from accepted graph lattice nodes; 2) selects one or more candidate graph lattice nodes preferentially discriminating exemplary data graphs which are less discriminable than other exemplary data graphs using the accepted graph lattice nodes; and 3) promotes the selected graph lattice nodes to accepted status. The graph lattice is formed from the accepted graph lattice nodes and relations between the accepted graph lattice nodes.

Abstract: A system and method classify a test image. At least one processor receives a data graph computed from the test image. Further, a graph lattice is received. The graph lattice includes a plurality of nodes, each including a subgraph, a weight and at least one mapping of the subgraph to data graphs of a plurality of training images. The training images correspond to a plurality of classes. The data graph of the test image is mapped by the subgraphs of the nodes. Mappings between the graph lattice and the data graphs of the training images are compared with mappings between the graph lattice and the data graph of the test image to determine weighted votes of similarity between the data graphs of the training images and the data graph of the test image. The class of the test image is determined from the weighted votes.

Abstract: A document recognition system and method, where images are represented as a collection of primitive features whose spatial relations are represented as a graph. Useful subsets of all the possible subgraphs representing different portions of images are represented over a corpus of many images. The data structure is a lattice of subgraphs, and algorithms are provided means to build and use the graph lattice efficiently and effectively.

Abstract: A method for electronically magnifying a target object with an imaging device. The method includes obtaining a full view image of the target object, where the full view image is focused. The method further includes moving the imaging device in proximity to a portion of the target object and obtaining a key image of the portion of the target object. The method further includes matching the key image to a corresponding portion of the full view image. The method further includes magnifying the corresponding portion of the full view image and displaying the magnified portion of the full view image.

Abstract: A system and method for communication in an online electronic chat environment having multiple communication devices connected to each other in a communication network is provided. Displayed on a display screen of an electronic communication device of the multiple communication devices, is a chat region configured to hold text, and a graphics region to hold graphic objects. The chat region and the graphics region are positioned on a common electronic canvas of the display screen. Text from the chat region can be moved to the graphics region, and graphic objects in the graphics region may be moved to the chat region. The design allows for the mixing of chat and graphics in a common window when material is moved between the two modalities. In additional embodiments, the text in the chat region and the graphics in the graphics region are synchronized whereby movement of one causes action in the other.

Abstract: A user interface and method is embodied on a computer readable medium and executable on a computer. The user interface is a labeler which labels only foreground pixels of an image stored in a computing environment. The labeler operates in a Region mode/state and Brush mode/state, and includes a Tentative mode that permits an assigned label to be changed after pixels have been selected. Groups of pixels may be selected for labeling at once by a point-and-click command, and a pixel may belong to one or more groups of pixels which are stored in memory as image layers. The groups are formed dynamically by user selection actions, and/or through automatic recognition algorithms. Pixels already labeled with certain labels may be locked to not be altered by additional labeling operations. Unassigned pixels may be highlighted to increase the ease at which they are identified in an image. Comparisons between labeled images are undertaken to indicate differences between different groundtruth labeling.

Abstract: Methods and systems for classifying markings on images in a document are undertaken according to marking types. The document containing the images is supplied to a segmenter which breaks the images into fragments of foreground pixel structures that are identified as being likely to be of the same marking type by finding connected components, extracting near-horizontal or -vertical rule lines and subdividing some connected components to obtain the fragments. The fragments are then supplied to a classifier, where the classifier provides a category score for each fragment, wherein the classifier is trained from the groundtruth images whose pixels are labeled according to known marking types. Thereafter, a same label is assigned to all pixels in a particular fragment, when the fragment is classified by the classifier.

Abstract: Methods of generating image anchor templates from low variance regions of document images of a first class are provided. The methods select a document image from the document images of the first class and align the other document images of the first class to the selected document image. Low variance regions are then determined by comparing the aligned document images and the selected document image and used to generate image anchor templates.