1437results about How to "Choose accurately" patented technology

A recognition tool according to various examples of the invention intelligently recognizes natural input before it is passed to a destination or target application. More particularly, the recognition tool according to various examples of the invention provides better formatting for text recognized from natural input, based upon the context in which the text is being inserted into a target application. The recognition tool also provides various tools for correcting inaccurately recognized text. The recognition tool may allow a user to select only a part of an inaccurate text, and then identify alternate text candidates based upon the selected portion of the inaccurate text. Further, when the user selects text containing multiple words for correction, the recognition tool provides cross combinations of alternate text candidates for the user's selection. Still further, if the user replaces inaccurate text by submitting a new natural input object, the recognition tool ensures that the text recognized from the new natural input object is different from the inaccurate text been replaced. The recognition tool additionally affects the recognition experience after recognized text has been provided to the target application. The recognition tool provides the target application with the original natural input object for the recognized text, along with the alternate text candidates for that original natural input object. Thus, the target application can use the alternate text candidates to correct inaccurately recognized text. Further, a user can insert the original natural input object for recognized text within the target application.

Systems and methods are disclosed for selecting target objects within digital images. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user indicators to select targeted objects in digital images. Specifically, the disclosed systems and methods can transform user indicators into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

An electronic camera displays recorded information, such as image frames, to permit the selection of items of information to be transferred to external equipment. Each time an item of information is transferred, an informative indication (e.g., an icon) is displayed in association with the transferred item to confirm the transfer and to permit an operator to select items of recorded information that may be erased. If an error occurs during transfer of a series of items, the operator can determine from the display the point at which the error occurred, avoiding the need for transferring items that have already been transferred.

A question document is divided into predetermined areas, and it is judged whether each divided area is important, to thereby extract an important area. A reply example candidate likelihood value is calculated for each important area, the likelihood value indicating the degree representative of whether each reply example candidate corresponds to a question content. By using the reply example candidate likelihood value, important areas having similar meanings are combined to extract final important parts. A reply example candidate is selected for each important part from reply example candidates prepared beforehand. A reply example candidate reliability degree representative of certainty of each reply example candidate and a reply composition degree indicating whether it is necessary to compose a new reply are calculated, and by using these values, question documents are distributed to different operator terminals.