Abstract:
Methods and systems for grapheme splitting of text input for recognition are provided. A method may include receiving a text input in a script and segmenting the text input into one or more graphemes. Each of the one or more graphemes may be split into one or more recognition units based on one or more recognition unit identification criteria associated with the script. Next, a text recognition system may be trained using the recognition units. Text input may be handwritten text input received from a user or a scanned image of text.
Abstract:
A method for displaying an aggregate count of endorsements is provided, including the following method operations: processing a request for an online resource from a mobile device, the online resource being associated with an object, the online resource including an endorsement mechanism; sending the online resource to the mobile device; processing an input from a user triggering the endorsement mechanism, to define an endorsement of the object by the user; updating an aggregate count of endorsements of the object to include the endorsement of the object by the user; sending the updated aggregate count of endorsements to the social display device for display on the social display device.
Abstract:
A social network server system may receive a social media message that is to be posted at the social network server system, the social media message being authored by a user of the social network server system. Prior to posting the social media message at the social network server system, the social network server system may determine, based at least in part on applying one or more rules to content of the social media message, a likelihood that the user would modify the content of the social media message after it is posted at the social network server system, wherein the one or more rules are generated based at least in part on previous actions taken by the user on previous social media messages authored by the user and posted at the social network server system and may, responsive to determining that the likelihood exceeds a threshold, generate an alert message.
Abstract:
Methods and systems for recognizing Devanagari script handwriting are provided. A method may include receiving a handwritten input and determining that the handwritten input comprises a shirorekha stroke based on one or more shirorekha detection criteria. Shirorekha detection criteria may be at least one criterion such as a length of the shirorekha stroke, a horizontality of the shirorekha stroke, a straightness of the shirorekha stroke, a position in time at which the shirorekha stroke is made in relation to one or more other strokes in the handwritten input, and the like. Next, one or more recognized characters may be provided corresponding to the handwritten input.
Abstract:
Systems and techniques are disclosed for selecting an optimal recognition for handwritten based on receiving a touch input from a user and applying both a delayed stroke recognizer as well as an overlapping recognizer to generate the recognition. A score may be generated for both the delayed stroke recognition as well as the overlapping recognition and the recognition corresponding to the highest score may be presented as the overall recognition.
Abstract:
A computer-implemented method includes: receiving, at a user device, user input corresponding to handwritten text to be recognized using a recognition engine; and receiving, at the user device, a representation of the handwritten text. The representation includes the handwritten text parsed into individual handwritten characters. The method further includes: displaying, on a display of the user device, the handwritten characters using a first indicator; receiving, at the user device, an identification of a text character recognized as one of the handwritten characters; displaying, on the display, the text character; and adjusting, at the user device, the one of the handwritten characters from being displayed using the first indicator to using a second indicator in response to the received identification. The first and second indicators are different.