Techniques and structures to facilitate conversion of a workflow process is disclosed. The techniques include receiving an image, identifying one or more objects included in the image, identifying one or more properties associated with each of the one or more objects, generating a matrix including data including the identified objects and associated properties and processing the matrix at a machine learning model to determine whether the image is to be translated based on a determination that one or more objects and associated properties within the image are required to be translated.

Aspects of the present disclosure include a system comprising a machine-readable storage medium storing at least one program and computer-implemented methods for detecting a language of a text string. Consistent with some embodiments, the method may include applying multiple language identification models to a text string. Each language identification model provides a predicted language of the text string and a confidence score associated with the predicted language. The method may further include weighting each associated confidence score based on historical performance of the corresponding language identification model in predicting languages of other text strings. The method may further include selecting a predicted language of the text string from among the multiple predicted languages provided by the multiple language identification models based on a result of the weighting of the confidence score associated with the particular predicted language.

An information processing system is communicable with a translation server through a network, and includes a receiver, circuitry, and a transmitter. The receiver receives content data indicating contents expressed in a first language and destination information indicating a destination to which the content data is to be transmitted. The circuitry determines, based on the destination information received by the receiver, a second language as a target language into which the contents expressed in the first language is to be translated. The transmitter transmits, to the destination indicated by the destination information, translated content data indicating contents that is translated by the translation server from the first language to the second language.

An information processing system is communicable with a translation server through a network, and includes a receiver, circuitry, and a transmitter. The receiver receives content data indicating contents expressed in a first language and destination information indicating a destination to which the content data is to be transmitted. The circuitry determines, based on the destination information received by the receiver, a second language as a target language into which the contents expressed in the first language is to be translated. The transmitter transmits, to the destination indicated by the destination information, translated content data indicating contents that is translated by the translation server from the first language to the second language.

Introduced here are communication optimization platforms configured to improve comprehension, persuasion, or clarity of communications. Initially, a communication optimization platform can acquire input sample(s) that are associated with a source audience. The communication optimization platform can then create a linguistic profile for the source audience by examining the content of the input sample(s). Additionally or alternatively, the communication optimization platform may produce a psychographic profile that specifies various characteristics of the source audience, such as personality, opinions, attitudes, interests, etc. The communication optimization platform can then generate, based on the linguistic profile and/or the psychographic profile, affinity language for communicating with a target audience. By incorporating the affinity language into communications, the communication optimization platform can increase appeal to the target audience.

Introduced here are communication optimization platforms configured to improve comprehension, persuasion, or clarity of communications. Initially, a communication optimization platform can acquire input sample(s) that are associated with a source audience. The communication optimization platform can then create a linguistic profile for the source audience by examining the content of the input sample(s). Additionally or alternatively, the communication optimization platform may produce a psychographic profile that specifies various characteristics of the source audience, such as personality, opinions, attitudes, interests, etc. The communication optimization platform can then generate, based on the linguistic profile and/or the psychographic profile, affinity language for communicating with a target audience. By incorporating the affinity language into communications, the communication optimization platform can increase appeal to the target audience.

Provided is a method for suggesting a caption for an image, comprising: receiving the image; determining a plurality of impacting categories associated with a plurality of contextual keywords for the image, each of the plurality of impacting categories representing a sentiment associated with the plurality of contextual keywords; grouping the plurality of contextual keywords into a plurality of groups based on the plurality of impacting categories; determining an order associated with the plurality of contextual keywords, based on a pre-determined impacting function; generating at least one caption by processing each contextual keyword, based on the order associated with the plurality of contextual keywords; determining a priority value associated with each of the at least one caption based on information associated with the corresponding caption, a user profile, and the image; and suggesting the caption from the at least one caption based on the priority value.

Provided is a method for suggesting a caption for an image, comprising: receiving the image; determining a plurality of impacting categories associated with a plurality of contextual keywords for the image, each of the plurality of impacting categories representing a sentiment associated with the plurality of contextual keywords; grouping the plurality of contextual keywords into a plurality of groups based on the plurality of impacting categories; determining an order associated with the plurality of contextual keywords, based on a pre-determined impacting function; generating at least one caption by processing each contextual keyword, based on the order associated with the plurality of contextual keywords; determining a priority value associated with each of the at least one caption based on information associated with the corresponding caption, a user profile, and the image; and suggesting the caption from the at least one caption based on the priority value.

Methods for automatic language detection for handwritten text are performed by systems and devices. Such automatic language detection is performed prior to sending representations of the handwritten text to a language recognition engine. Handwritten inputs including one or more writing strokes are received from an input interface, and are associated with coordinates of the inputs and times that the inputs are made. The handwritten inputs are grouped into words based on the coordinates and times. Writing strokes are normalized, and then the words are individually transformed to generate language vectors, such as through a recurrent neural network. The language vectors are used to determine language probabilities for the handwritten inputs. Based on the language probabilities, the handwritten inputs are provided to a specific language recognition engine to determine the language thereof prior to translation or transcription.

Aspects include receiving a document and classifying at least a subset of the document as having a first type of data. Features are extracted from the document. The extracting includes initiating processing of the at least a subset of the document by a first processing engine that was previously trained to extract features from the first type of data. The extracting also includes initiating processing of a remaining portion of the document not included in the at least a subset of the document by a second processing engine that was previously trained to extract features from a second type of data. The first type of data is different than the second type of data. Features are received from one or both of the first processing engine and the second processing engine. The received features are stored as features of the document.