A machine vision device is provided including a camera, a processor, and a device memory including computer program code stored thereon. The computer program code is configured, when executed by the processor, to receive an image, from the camera, including at least one readable digital label associated with communication equipment, determine if an anchor label is present in the image, receive equipment information based on the anchor label and generate a search matrix based on the equipment information and the anchor label The search matrix includes one or more search matrix locations of assets associated with the communication equipment.

A machine vision device is provided including a camera, a processor, and a device memory including computer program code stored thereon. The computer program code is configured, when executed by the processor, to receive an image, from the camera, including at least one readable digital label associated with communication equipment, determine if an anchor label is present in the image, receive equipment information based on the anchor label and generate a search matrix based on the equipment information and the anchor label The search matrix includes one or more search matrix locations of assets associated with the communication equipment.

A method for recognizing gesture strokes in user input, comprising: receiving data generated based on the user input, the data representing a stroke and comprising a plurality of ink points in a rectangular coordinate space and a plurality of timestamps associated respectively with the plurality of ink points; segmenting the plurality of ink points into a plurality of segments each corresponding to a respective sub-stroke of the stroke and comprising a respective subset of the plurality of ink points; generating a plurality of feature vectors based respectively on the plurality of segments; and applying the plurality of feature vectors as an input sequence representing the stroke to a trained stroke classifier to generate a vector of probabilities including a probability that the stroke is a non-gesture stroke and a probability that the stroke is a given gesture stroke of a set of gesture strokes.

A method for recognizing gesture strokes in user input, comprising: receiving data generated based on the user input, the data representing a stroke and comprising a plurality of ink points in a rectangular coordinate space and a plurality of timestamps associated respectively with the plurality of ink points; segmenting the plurality of ink points into a plurality of segments each corresponding to a respective sub-stroke of the stroke and comprising a respective subset of the plurality of ink points; generating a plurality of feature vectors based respectively on the plurality of segments; and applying the plurality of feature vectors as an input sequence representing the stroke to a trained stroke classifier to generate a vector of probabilities including a probability that the stroke is a non-gesture stroke and a probability that the stroke is a given gesture stroke of a set of gesture strokes.

An information processing apparatus includes a processor configured to: obtain image data; obtain information including at least one of setting information set in advance for optical character recognition processing by plural apparatuses capable of communicating with the information processing apparatus or attribute information of each of the plural apparatuses; and based on the obtained image data and the obtained information, determine an apparatus used for optical character recognition processing of the image data from among the plural apparatuses.

An information processing apparatus includes a processor configured to: obtain image data; obtain information including at least one of setting information set in advance for optical character recognition processing by plural apparatuses capable of communicating with the information processing apparatus or attribute information of each of the plural apparatuses; and based on the obtained image data and the obtained information, determine an apparatus used for optical character recognition processing of the image data from among the plural apparatuses.

In a scene where a pseudo character image is generated by performing deformation processing for a character image, a character image that impedes training is suppressed from being generated. Based on a condition relating to a parameter that is used for the deformation processing and associated with a first class, a parameter of the deformation processing is determined and the deformation processing is performed for a character image belonging to the first class using the determined parameter. Then, whether or not the deformed character image generated by the deformation processing is similar to a character image belonging to a class different from the first class is determined and in a case where similarity is determined, the condition associated with the first class is updated.

In a scene where a pseudo character image is generated by performing deformation processing for a character image, a character image that impedes training is suppressed from being generated. Based on a condition relating to a parameter that is used for the deformation processing and associated with a first class, a parameter of the deformation processing is determined and the deformation processing is performed for a character image belonging to the first class using the determined parameter. Then, whether or not the deformed character image generated by the deformation processing is similar to a character image belonging to a class different from the first class is determined and in a case where similarity is determined, the condition associated with the first class is updated.

A method for data collection and analysis may include generating one or more screenshots; converting the screenshot, via OCR, into one or more strings of computer-readable text; detecting one or more variables from the computer-readable text; retrieving, additional variables via one or more modules; and displaying, via a video display device, a dashboard. The dashboard may include one or more configurable views, and each configurable view may include a visual representation of one or more variables.

A method for data collection and analysis may include generating one or more screenshots; converting the screenshot, via OCR, into one or more strings of computer-readable text; detecting one or more variables from the computer-readable text; retrieving, additional variables via one or more modules; and displaying, via a video display device, a dashboard. The dashboard may include one or more configurable views, and each configurable view may include a visual representation of one or more variables.