Abstract: Audio item(s) that may be of interest to a user can be selected from a larger collection of audio items. The audio items of interest may be identified by concurrently generating audio from each item in the collection. The audio generated from individual items in the collection may be generated such that the audio is audibly and selectably differentiable from the audio generated from other items in the collection. A user-input may be detected that corresponds to a selection of a subset of the audio items. A user-input may be detected that modifies characteristics of the audio presentation in space and/or volume. A correlation between the input and the selected audio may be made through characteristics that are incorporated into the selected audio when that audio is made distinguishable.

Abstract: The invention improves the appearance of freehand drawn lines and shapes in an electronic document by first recognizing freehand drawn lines and shapes and generating a line made up of sequential straight line segments for the freehand drawn line when the line does not form a closed line and generating a multiple straight-line-segment shape when the line forms a closed line. If a multiple segment shape is being reshaped, a basic shape is selected from reference ideal shapes as the basic shape of the multiple segment shape. The basic shape is adjusted to provide a specific shape as an improved shape for the freehand drawn shape. The recognition of the freehand drawn lines and shapes is accomplished by comparing source segments of a source freehand drawn line to a straight line and substituting a straight line segment for a source segment if the deviation between the source segment and the straight line is below a predetermined value.

Abstract: A set of source points that represent a stroke input of a user is identified. The set of source points may be refined and/or modified. The set of refined/modified source points may then be stored in memory for decoding and recreation of a stroke representation. Additionally, one or both of refining and modifying the source points may be performed through one or more upsampling processes.

Abstract: Character model graphs are created, and the parameters of the model graphs are adjusted to optimize character recognition performed with the model graphs. In effect the character recognizer using the model graphs is trained. The model graphs are created in three stages. First, a vector quantization process is used on a set of raw samples of handwriting symbols to create a smaller set of generalized reference characters or symbols. Second, a character reference model graph structure is created by merging each generalized form model graph of the same character into a single character reference model graph. The merging is based on weighted Euclidian distance between parts of trajectory assigned to graph edges. As a last part of this second stage “type-similarity” vectors are assigned to model edges to describe similarities of given model edge to each shape and to each possible quantized value of other input graph edge parameters.

Abstract: A character recognizer recognizes a handwritten input character. A sequence of points in two dimensional space representative of a stroke trajectory forming the input character is gathered. An input Directed Acyclic Graph is built with nodes representative of singular points at the beginning, end, and along the trajectory of the input character and with edges between nodes representative of an edge trajectory formed by the sequence of points of the input character between the singular points. Each edge in the input graph is described based on the shape, orientation and pen lift of the edge trajectory that the edge represents. The input graph is evaluated against model graphs, which are also Directed Acyclic Graphs, for all possible characters to find a path through a model graph that produces a best path similarity score with a corresponding path through the input graph. The input character is identified as an answer character represented by the model graph producing the best path similarity score.