An object is recognized from image data as a target object and linked to a user based on an interaction by the user, information about the target object is obtained and a purchase of the target object is initiated.

Various embodiments disclosed herein are directed to methods of capturing Vehicle Identification Numbers (VIN) from images captured by a mobile device. Capturing VIN data can be useful in several applications, for example, insurance data capture applications. There are at least two types of images supported by this technology: (1) images of documents and (2) images of non-documents.

According to an embodiment, a recognition device includes a candidate detection unit, a recognition unit, a matching unit, and a prohibition processing unit. The candidate detection unit detects, from an input image, character candidates each being a set of pixels estimated to include a character. The recognition unit recognizes each of the character candidates and generates one or more recognition candidates each being a character of a candidate as a recognition result. The matching unit matches each of the one or more recognition candidates with a knowledge dictionary in which a recognition target character string is modeled, and generates matching results obtained by matching a character string estimated to be included in the input image with the knowledge dictionary. The prohibition processing unit deletes, from the matching results, a matching result obtained by matching a character string including a prohibition target character string with the knowledge dictionary.

According to an embodiment, a recognition device includes a detector, a recognizer, and a matcher. The detector is configured to detect a character candidate from an input image. The recognizer is configured to generate recognition candidate from the character candidate. The matcher is configured to match the recognition candidate with a knowledge dictionary and contains modeled character strings to be recognized, and generate a matching result obtained by matching a character string presumed to be included in the input image with the dictionary. Any one of a real character code that represents a character and a virtual character code that specifies a command is assigned to an edge. The matcher gives, when shifting a state of the dictionary in accordance with an edge to which the virtual character code is assigned, a command specified by the virtual character code assigned to the edge to a command processor.

Methods and systems for testing base text direction (BTD) include receiving one or more images captured by an end-user system. Each of the one or more images displays respective text test case information. Each of the one or more images is compared to a respective reference image associated with a respective text test case. It is determined whether the end-user system produces BTD errors based on the comparison in accordance with one or more BTD error rules.

A method for combining a plurality of images into a synthesis image and an electronic device implementing the same are provided. The image synthesis method of the present disclosure includes acquiring coordinates of an object in a source image; extracting a target image from the source image based on the coordinates such that the target image contains the object; and displaying the target image in a section of a frame mapped to the source image.

Various embodiments of a method and system for capturing and utilizing item attributes are described. Various embodiments may include a mobile image capture apparatus, which may include a computer system configured to execute an image capture application. The image capture application may instruct an agent to capture an image of an item label. A data extraction component may be configured to process the images captured by the mobile image capture apparatus. For a given captured image, the data extraction component may in various embodiments be configured to perform OCR to determine one or more strings of characters from the image. The data extraction component may be further configured to determine whether one or more patterns match a determined string of characters. In response to the detection of a particular pattern matching a particular string of characters, the data extraction component may extract and store an attribute of the corresponding item.

An automatic body movement recognition and association system that includes a preprocessing component and a “live testing” engine component. The system further includes a transition posture detector module and a recording module. The system uses three dimensional (3D) skeletal joint information from a stand-alone depth-sensing capture device that detects the body movements of a user. The transition posture detector module detects the occurrence of a transition posture and the recording module stores a segment of body movement data between occurrences of the transition posture. The preprocessing component processes the segments into a preprocessed movement that is used by a classifier component in the engine component to produce text or speech associated with the preprocessed movement. An “off-line” training system that includes a preprocessing component, a training data set, and a learning system also processes 3D information, off-line from the training data set or from the depth-sensing camera, to continually update the training data set and improve a learning system that sends updated information to the classifier component in the engine component when the updated information is shown to improve accuracy.

A computer-implemented technique includes techniques are presented for user image capture feedback for improved machine language translation. When machine language translation of OCR text obtained from an initial image has a low degree of likelihood of being an appropriate translation, these techniques provide for user image capture feedback to obtain additional images to obtain a modified OCR text, which can result in improved machine language translation results. Instead of user image capture feedback, the techniques may obtain the modified OCR text by selecting another possible OCR text from the initial OCR operation. In addition to additional image capturing, light source intensity and/or a quantity/number of light source flashes can be adjusted. After obtaining the modified OCR text, another machine language translation can be obtained and, if it has a high enough degree of likelihood, it can then be output to a user.

A method implemented by computer for the semantic description of the content of an image comprising the steps consisting in receiving a signature associated with the image; receiving a plurality of groups of initial visual concepts; the method comprising the steps of expressing the signature of the image in the form of a vector comprising components referring to the groups of initial visual concepts; and modifying the signature by applying a filtering rule applicable to the components of the vector. Developments describe, in particular, intra-group or inter-group, thresholds-based and/or order-statistic-based filtering rules, partitioning techniques including the visual similarity of the images and/or semantic similarity of the concepts, the optional addition of manual annotations to the semantic description of the image. The advantages of the method in respect of parsimonious and diversified semantic representation are presented.