Abstract: A method for improving the accuracy of speech recognition is disclosed. In one embodiment, such a method receives speech input from a user. The method further receives background inputs describing at least one of a webpage and an application from which the speech input was received. The method determines a language associated with the speech input and determines a weight and confidence level for each of the background inputs. A score is calculated for each of the background inputs based on the corresponding weight and confidence level. The method determines textual candidates for output in response to the speech input and ranks the textual candidates using a function that takes into account the scores and/or confidence levels of the background inputs. The textual candidate with the highest ranking may be returned to the user. A corresponding system and computer program product are also disclosed.

Abstract: A computer-implemented method, according to one embodiment, includes determining a domain and category information from metadata associated with a first webpage. The method further includes determining component types, hierarchies and grouping relationships for a plurality of text elements on the first webpage and constructing context information for the text elements. Word vectors are calculated based on the grouping relationships and the context information, and text feature types of the text elements are extracted based on the word vectors and the context information. The method further includes using the extracted text feature types to determine a re-ordering of a translation of the text elements. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: From an input image comprising a portion of text, the portion of text is extracted. The portion of text comprises text in a first natural language. The input image is classified into a candidate scene within a set of predefined scenes. The candidate scene is scored according to a quality measure. Using a predefined scene parameter of the scored candidate scene, the portion of text is translated. In the input image, the portion of text is replaced. with the translated portion of text.

Abstract: Aspects of the present disclosure relate generally to UI automation in RPA and, more particularly, to automation of contextual UI element identification by RPA robots. For example, a computer-implemented method includes: receiving, by a processor set, user interface context identification information of attributes of user interface elements in user interface code of an application; constructing, by the processor set, a model of the attributes of the user interface elements including an attribute indicating functionality of at least one user interface element; obtaining from the model of the attributes of the user interface elements an identification of the at least one user interface element referenced in an action command for performing the functionality of the at least one user interface element in robotic process automation code; and storing, by the processor set, the model of the attributes of the user interface elements in persistent storage.

Abstract: From an input image comprising a portion of text, the portion of text is extracted. The portion of text comprises text in a first natural language. The input image is classified into a candidate scene within a set of predefined scenes. The candidate scene is scored according to a quality measure. Using a predefined scene parameter of the scored candidate scene, the portion of text is translated. In the input image, the portion of text is replaced. with the translated portion of text.

Abstract: The present disclosure relates to a method and system for deploying user interface elements on a screen which has broken-faulty regions on the screen. One or more broken-faulty regions are detected on the screen. At least a part of the normal regions on the screen is determined to be a region for displaying at least a part of one or more user interface elements which are related to the one or more broken-faulty regions. The at least a part of the one or more user interface elements are deployed on the screen based on the result of the determining.

Abstract: The present disclosure relates to a method and system for deploying user interface elements on a screen which has broken-faulty regions on the screen. One or more broken-faulty regions are detected on the screen. At least a part of the normal regions on the screen is determined to be a region for displaying at least a part of one or more user interface elements which are related to the one or more broken-faulty regions.