A pattern recognition locking system and method is operable to lock and unlock an object through a sequence of knocks, rotations, and ambient vibrations that are measured by an accelerometer as acceleration, and a gyroscope as orientation. The sequence and intensity of vibrations and intensity are processed by a microcontroller that utilizes a pattern recognition algorithm to: recognize the intensity and sequence of acceleration and orientation; identify the most intense motion from the degrees of freedom; and, after identifying a sequence of the most intense motions for respective degree of freedom, the microcontroller transmits a signal to lock mechanism to lock or unlock an object if sequence of degrees of motion match a stored passcode. A sleep module powers off the system when no motion or vibrations detected. A new sequence of ambient vibrations and motion are created through a learning module. A reset switch reprograms the sequence of motions.

Disclosed herein are computer-implemented methods; computer-implemented systems; and non-transitory, computer-readable media, for quantifying text similarity. One computer-implemented method includes obtaining a plurality of shortest operation paths including one or more edit pairs for correcting an optical correction recognition (OCR) text string with an edit text string, where each of the one or more edit pairs denotes an operation performable to a character of the OCR text string during correction by the edit text string. A plurality of similarity scores is determined, each corresponding to one of the plurality of shortest operation paths and determined by summing historical similarity scores of the one or more edit pairs of each of the plurality of shortest operation paths. A minimum one of the plurality of similarity scores is selected to quantify text similarity between the OCR text string and the edit text string.

An image-processing device includes: a reliability calculation unit configured to calculate reliability of a character recognition result for a document image which is a character recognition target on the basis of a descriptive feature amount of a character string of a specific item included in the document image; and an image output unit configured to output an image of the character recognition result indicating the character string of the specific item in a display mode in accordance with the reliability.

A computer-implemented method for processing a digital image. The digital image comprises one or more text cells, wherein each of the one or more text cells comprises a string and a bounding box. The method comprises receiving the digital image in a first format, the first format providing access to the strings and the bounding boxes of the one more text cells. The methods further comprises encoding the strings of the one or more text cells as visual pattern according to a predefined string encoding scheme and providing the digital image in a second format. The second format comprises the visual pattern of the strings of the one or more text cells. A corresponding system and a related computer program product is provided.

A character recognition system converting image data of a character string into a character code is provided, which includes a data processing unit, and a storage unit configured to be used by the data processing unit, wherein the storage unit stores similar-outline nonsense character string data that has an outline similar to that of to an ordinary character string but does not make sense, and the data processing unit performs character recognition processing using the similar-outline nonsense character string data that is stored in the storage unit.

Provided are a multi-pattern policy detection system and method, wherein, in an environment that operates a plurality of policies for determining matching or non-matching by a string or a normalized format, the plurality of policies are expressed by a data structure that is searchable at a time, and are optimized to improve search performance. The multi-pattern policy detection system includes: a search front stage optimizer configured to register a string of a signature fragment received from a signature fragment list as a registration pattern of a front stage of a signature by taking into account length and uniqueness of the string; a search rear stage optimizer configured to receive the signature fragment from the signature fragment list, and register the signature fragment as a registration pattern of a rear stage when there is no registration signature fragment of the rear stage; and a detection engine configured to perform attack detection by using the front stage of the search front stage optimizer and the rear stage of the search rear stage optimizer.

Data from one or more sensors is input to a workflow and fragmented to produce HyperFragments. The HyperFragments of input data are processed by a plurality of Distributed Experts, who make decisions about what is included in the HyperFragments or add details relating to elements included therein, producing tagged HyperFragments, which are maintained as tuples in a Semantic Database. Algorithms are applied to process the HyperFragments to create an event definition corresponding to a specific activity. Based on related activity included in historical data and on ground truth data, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with the current input data to more accurately detect when the specific activity is being carried out.

An image-processing device includes: a character string detection unit configured to detect a character string of a specific item in a first document image based on a feature amount of the displayed first document image among feature amounts which are recorded in advance based on a result of learning using a plurality of document images and indicate features of the character string of the item for each kind of document image and each specific item; and an output unit configured to output information regarding a correspondence relation indicating the same specific item between the first document image and a second document image displayed to correspond to the first document image.

In a situation of setting a file name and the like by using a character string obtained by performing OCR processing to a scan image, appropriate conditions can be set according to a character string to be scanned so as to increase a character recognition rate. There is provided an apparatus for performing a predetermined process to a scan image obtained by scanning a document, including: a display control unit configured to display a UI screen for performing the predetermined process, the UI screen displaying a character area assumed to be one continuous character string in the scan image in a selectable manner to a user; and a setting unit configured to determine a condition for OCR processing based on selection order of a character area selected by a user via the UI screen and a format of supplementary information for the predetermined process, perform OCR processing by using the determined condition for OCR processing to the selected character area, and set supplementary information for the predetermined process by using a character string extracted in the OCR processing.

A computing system may include a client computing device configured to execute a software application with an associated GUI. The GUI includes fields, and each field is to hold a text string. The computing system may include a GUI testing device in communication with the client computing device and configured to execute a testing framework for interacting with the software application to generate versions of the GUI, each of the versions being in a different language, and defining expected text strings in the fields. The GUI testing device may be configured to extract the fields from the versions of the GUI, perform OCR processing on the fields to generate actual text strings, and compare the actual text strings with the expected text strings.