Kabushiki Kaisha Toshiba

Toshiba Solutions Corporation

Toshiba Digital Solutions Corporation

Soichiro Ono

Tomohisa Suzuki

Hiroyuki Mizutani

Mayumi Yuasa

Shinichiro Hamada

Kunio Osada

Akio Furuhata

Atsuhiro Yoshida

According to an embodiment, a data management system includes an index building unit and a searching unit. The index building unit generates a peripheral vector similar to a case example vector representing a feature vector of data to be stored, and builds index information of enabling identification of the case example vector corresponding to the generated peripheral vector. The searching unit refers to the index information in response to a search request in which a query vector representing an arbitrary feature vector is specified, identifies the case example vector corresponding to the peripheral vector that exactly matches with the query vector, and outputs a search result based on the identified case example vector.

Data management system, data management method, and computer program product

In an image processing system, an image processing apparatus configured to recognize images of documents is connected through a network to terminal apparatuses each including an input unit and a display unit. The image processing apparatus includes: a recognition unit configured to perform character recognition processing on an image; a confidential information detecting unit configured to detect confidential information from a result of the character recognition processing; and a manipulation unit configured to generate, based on the confidential information in the image, a first manipulated image obtained by fragmenting the confidential information. Each of the terminal apparatuses includes: a display unit configured to display the first manipulated image; and an input unit configured to input corrected data for the first manipulated image.

Image processing apparatus, image processing system, computer program product, and image processing method

According to an embodiment, a pattern recognition device is configured to divide an input signal into a plurality of elements, convert the divided elements into feature vectors having the same dimensionality to generate a set of feature vectors, and evaluate the set of feature vectors using a recognition dictionary including models corresponding to respective classes, to output a recognition result representing a class or a set of classes to which the input signal belongs. The models each include sub-models each corresponding to one of possible division patterns in which a signal to be classified into a class corresponding to the model can be divided into a plurality of elements. A label expressing a model including a sub-model conforming to the set of feature vectors, or a set of labels expressing a set of models including sub-models conforming to the set of feature vectors is output as the recognition result.

Pattern recognition device, pattern recognition method, and computer program product

According to an embodiment, a pattern recognition device recognizes a pattern of an input signal by converting the input signal to a feature vector and matching the feature vector with a recognition dictionary. The recognition dictionary includes a dictionary subspace basis vector for expressing a dictionary subspace which is a subspace of a space of the feature vector, and a plurality of probability parameters for converting similarity calculated from the feature vector and the dictionary subspace into likelihood. The device includes a recognition unit configured to calculate the similarity using a quadratic polynomial of a value of an inner product of the feature vector and the dictionary subspace basis vector, and calculate the likelihood using the similarity and an exponential function of a linear sum of the probability parameters. The recognition dictionary is trained by using an expectation maximization method using a constraint condition between the probability parameters.

According to an embodiment, an information processing device includes a receiving unit, a detection unit, a generation unit, and an output unit. The receiving unit is configured to receive an original image. The detection unit is configured to detect a write region in which additional information is to be written, from the original image. The generation unit is configured to generate a position specifying image for specifying a position of the write region in the original image, the position specifying image being to be added to a predetermined region in the original image. The output unit is configured to output the position specifying image.

Information processing device, information processing method, and computer program product

An image recognition apparatus according to one aspect of the present invention has a measurement unit measuring a blur level of an image, a comparison unit comparing the blur level measured in the measurement unit with a threshold, an image processing unit applying to the image a blurring filter which increases the blur level when the blur level measured in the measurement unit is smaller than the threshold, and applying to the image a deblurring filter which decreases the blur level when the blur level measured in the measurement unit is larger than the threshold, and a recognition unit recognizing the image from features of the image processed in the image processing unit.

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Kawasaki Kanagawa, Japan

Soichiro Ono