Fujitsu Corporation

Katsuhito Fujimoto

Hiroaki Takebe

Satoshi Naoi

Yoshinobu Hotta

Akihiro Minagawa

Atsuko Ohara

Noriaki Ozawa

Hiroshi Kamada

Yusaku Fujii

Hiroshi Tanaka

Koji Kurokawa

Misako Suwa

Nobuyuki Hara

Yutaka Katsuyama

Tatsushi Otsuka

Taichi Murase

Hideki Yoshizawa

Atsunori Moteki

Jun Sun

Seiji Takahashi

Toru Kamiwada

Takehiro Nakai

Kenji Nakajima

Masashi Urushihara

Takashi Terasaki

Yoshio Fukasawa

Junji Kashioka

Masaki Inami

Kiichiro Watanabe

Xu-Cheng Yin

Yusaku Fuji

Junichi Hirai

An image processing method includes recognizing a first object that is included in the image data, calculating a positional variation amount of a feature point that corresponds to the first object and is moving in an image center direction so as to calculate a moving speed of the first object on the basis of the positional variation amount, determining whether or not the first object is a gaze target object of the user, in accordance with a behavior of the first object, the behavior being obtained on the basis of the positional variation amount of the first object among the plurality of image data, of which acquisition time is respectively different and whether or not the object continuingly exists in a second region, the second region being positioned in an inside of the first region and including a center point of the image data, for a predetermined period of time.

Image processing device, image processing method, and a computer-readable non-transitory medium

An information processing apparatus includes an image capturing section to capture an image of a hand; an extracting section to extract a hand area from the captured image; a reference line determining section to determine a reference pushdown line in the image on the hand area; a determining section to determine a pushdown move if the bottom part of the hand area comes below the reference pushdown line; a first position determining section to determine a depth position based on an aspect ratio of the hand area if the pushdown move is determined; a second position determining section to determine a lateral position based on a position of the bottom part of the hand area if the pushdown move is determined; and an input key determining section to determine an input key from the determined depth position and lateral position.

Information processing apparatus and input control method

An image processing device that accesses a storage unit that stores a feature point of a recognition-target object, the device includes an obtaining unit mounted with a user and configured to obtain image data in a direction of a field of view of the user; a recognizing unit configured to recognize the recognition-target object included in the image data by extracting a feature point from the image data and associating the extracted feature point and the feature point of the recognition-target object stored in the storage unit with each other; a calculating unit configured to calculate a location change amount of the feature point corresponding to the recognition-target object recognized by the recognizing unit from a plurality of the image data obtained at different times and calculate a motion vector of the recognition-target object from the location change amount; and a determining unit configured to determine a movement.

Image processing device and method

According to an aspect of an embodiment, a method of detecting boundary line information contained in image information comprising a plurality of pixels in either one of first and second states, comprising: detecting a first group of pixels in the first state disposed continuously in said image information to determine first line information and detecting a second group of pixels in the first state disposed adjacently with each other and surrounded by pixels in the second state to determine edge information based on the contour of the second group of pixels; and determining the boundary line information on the basis of the information of the relation of relative position of the line information and the edge information and the size of the first and second group of pixels.

Method and apparatus for recognizing boundary line in an image information

A program causes a computer to function as a document recognition apparatus, having an extraction unit for extracting connected components of pixels from an input image, a generation unit for generating a reference element that is connected components of pixels extracted by the extraction unit and combined elements obtained by combining the reference element and connected components of pixels adjacent to the reference element as an element to be estimated, a calculation unit for calculating a degree of certainty that indicates how much the element to be estimated generated by the generation unit seems to be a character, and a determination unit for identifying elements that seem to be characters among the elements to be estimated based on the degree of certainty calculated by the calculation unit.

Storage medium, apparatus and method for recognizing characters in a document image using document recognition

An image recognition apparatus recognizes the correspondence between character strings and logical elements composing a logical structure in an image in which the character strings are described as the logical elements to recognize each logical element. The image recognition apparatus includes outputting means for outputting the recognized logical elements when the correspondence is recognized or re-recognized; first determining means for determining a certain logical element to be correct when input of a determination request to determine the logical element is received from a user; second determining means for determining the correctness of all the logical elements output before the logical element determined by the first determining means and is positioned according to confirmation by the user; and re-recognizing means for re-recognizing the correspondence between logical elements that have not been determined to be correct and the character strings on the basis of the determination content for each logical element.

Image recognition apparatus, image recognition method, and storage medium recording image recognition program

A character area extracting device includes a reflective and non-reflective area separation unit separating image data into reflective and non-reflective areas, and binarizing the image data by changing a first threshold value when it is inappropriate; a reflective area binarizing unit separating the reflective area into character and background areas, and binarizing it by changing a second threshold value when it is inappropriate; a non-reflective area binarizing unit separating the non-reflective area into the character and background areas, and binarizing it by changing a third threshold value when it is inappropriate; a reflective and non-reflective area separation evaluation unit; and a line extracting unit connecting the character areas of the reflective and non-reflective areas and extracting positional information of the connected character areas in the image data.

Character area extracting device, imaging device having character area extracting function, recording medium saving character area extracting programs, and character area extracting method

The form data extracting apparatus, even input form does not have a logical structure stored in the generic logical structure DB, by using logical elements in the existing logical structure and a registered form obtained on the basis of (a) the logical structure, (b) pieces of position information of the logical elements, and (c) a relation between the logical elements. A logical element and a logical structure are extracted from the input form, and the extracted logical structure can be defined as a new registered form or a new logical structure.

Computer readable recording medium on which form data extracting program is recorded, form data extracting apparatus, and form data extracting method

A dictionary creating apparatus registers probability distributions each including an average vector and a covariance matrix, in a dictionary. The dictionary creating apparatus organizes plural distribution profiles of character categories having similar feature vectors into one typical distribution profile, and registers the typical distribution profile and the character categories to be organized, associated with each other, in the dictionary, without registering eigenvalues and eigenvectors of all character categories, associated with each other, in the dictionary.

Dictionary creating apparatus, recognizing apparatus, and recognizing method

An area extraction method including obtaining a character lattice showing a connection relation between unit areas, which are obtained by separating a character string pattern in an image into patterns each recognized as corresponding to a single character, judging whether or not all combinations of each of the unit areas in the obtained character lattice and each of the unit areas in a regular lattice defining a regular connection relation between the unit areas are likely to be established, generating a path coupling between nodes corresponding to the combination of the unit areas which is determined as likely to be established, determining an optimum path from the generated paths based on a degree of coincidence with the regular lattice or the character lattice, and extracting from an image the unit areas in the character lattice corresponding to the determined optimum path.

Area extraction program, character recognition program, and character recognition device

A document type identifying apparatus includes in advance a database storing therein keywords used as keys that identify document types in association with each document type. The document type identifying apparatus aligns word strings written on a document and generates partial keyword strings for each keyword by using the keywords stored in the database. The partial keyword strings are to be checked for matching with the word strings written on the document. Then, the document type identifying apparatus checks matching of the grouped and aligned word strings with the partial keyword strings and obtains, for each keyword, each number of matched words with the highest matching rates between the grouped word strings that are successfully matched and the partial keyword strings. Then, each number of matched words is used to calculate each evaluation value to determine the document type.

Document type identifying method and document type identifying apparatus

This method includes: extracting a feature vector for an input character from a reading result of the input character; calculating distances between the feature vector for the input character and vectors including average vectors stored in a system dictionary storing, for each character, the average vector and distribution information, and feature vectors stored in a user dictionary; extracting the top N character codes in an ascending order of the calculated distances; obtaining second distribution information for the character codes, which are included the user dictionary and in the top N character codes; calculating, for each of the top N character codes, a second distance with the feature vector for the input character, by using, for the character codes, which are included in the user dictionary and in the top N character codes, the second distribution information; and identifying a character code whose second distance is shortest.

Character recognition processing method and apparatus

A method for assisting in the creation of a logical structure model, which stores, from an image in which character strings associated respectively with a plurality of logical elements constituting a logical structure are described, the logical elements, character strings associated with the logical elements, and the logical structure, wherein character strings in an input image and the logical structure among the character strings in the input image are extracted, a logical element is selected among the plurality of logical elements according to the degrees of similarity between the extracted character strings and the character string associated respectively with the plurality of logical elements stored in the logical structure model, a character string associated with the selected logical element and a character string in the input image associated with the logical element based on the logical structure among the extracted character strings in the input image are extracted.

Recording medium for recording logical structure model creation assistance program, logical structure model creation assistance device and logical structure model creation assistance method

An image recognition method is conducted by recognizing logical elements based on a logical structure model set to correspond to the logical structure of an image of individual character strings, collecting information processed with the logical structure model of images of a logical structure, acquiring a recognition result when recognizing an image of a logical structure by processing information collected with a post-update logical structure model, and outputting warning information about the post-update logical structure model to an output unit when a result of the comparison is a non-match.

Image recognition apparatus, method and system for realizing changes in logical structure models

According to an aspect of an embodiment, an apparatus for analyzing and determining correlation of information contained in a given form containing blocks, at least one of the blocks containing data indicative of a header, the rest of the blocks containing data in association with header information, comprising: a memory for storing templates having nodes, character data associated with said nodes respectively, and relative position information between said nodes; and a processor for analyzing and determining correlation of the information according to a process comprising: obtaining data contained in said blocks in the given form, determining relative position of said blocks to produce relative position information, analyzing the data obtained from the blocks and the relative position information of the blocks in comparison with the character data and the relative position information of said nodes of said templates, and determining correlation of the data contained in said blocks.

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Kawasaki, Japan

Katsuhito Fujimoto