587 results about "Handwriting recognition" patented technology

A hybrid approach to improve handwriting recognition and voice recognition in data process systems is disclosed. In one embodiment, a front end is used to recognize strokes, characters and / or phonemes. The front end returns candidates with relative or absolute probabilities of matching to the input. Based on linguistic characteristics of the language, e.g. alphabetical or ideographic language for the words being entered, e.g. frequency of words and phrases being used, likely part of speech of the word entered, the morphology of the language, or the context in which the word is entered), a back end combines the candidates determined by the front end from inputs for words to match with known words and the probabilities of the use of such words in the current context.

A computer-implemented method for providing a candidate list of alternatives for a text selection containing text from multiple input sources, each of which can be stochastic (such as a speech recognition unit, handwriting recognition unit, or input method editor) or non-stochastic (such as a keyboard and mouse). A text component of the text selection may be the result of data processed through a series of stochastic input sources, such as speech input that is converted to text by a speech recognition unit before being used as input into an input method editor. To determine alternatives for the text selection, a stochastic input combiner parses the text selection into text components from different input sources. For each stochastic text component, the combiner retrieves a stochastic model containing alternatives for the text component. If the stochastic text component is the result of a series of stochastic input sources, the combiner derives a stochastic model that accurately reflects the probabilities of the results of the entire series. The combiner creates a list of alternatives for the text selection by combining the stochastic models retrieved. The combiner may revise the list of alternatives by applying natural language principles to the text selection as a whole. The list of alternatives for the text selection is then presented to the user. If the user chooses one of the alternatives, then the word processor replaces the text selection with the chosen candidate.

Techniques are provided for determining collaborative notes and automatically recognizing speech, handwriting and other type of information. Domain and optional actor / speaker information associated with the support information is determined. An initial automatic speech recognition model is determined based on the domain and / or actor information. The domain and / or actor / speaker language model is used to recognize text in the speech information associated with the support information. Presentation support information such as slides, speaker notes and the like are determined. The semantic overlap between the support information and the salient non-function words in the recognized text and collaborative user feedback information are used to determine relevancy scores for the recognized text. Grammaticality, well formedness, self referential integrity and other features are used to determine correctness scores. Suggested collaborative notes are displayed in the user interface based on the salient non-function words. User actions in the user interface determine feedback signals. Recognition models such as automatic speech recognition, handwriting recognition are determined based on the feedback signals and the correctness and relevance scores.

A word pattern recognition system based on a virtual keyboard layout combines handwriting recognition with a virtual, graphical, or on-screen keyboard to provide a text input method with relative ease of use. The system allows the user to input text quickly with little or no visual attention from the user. The system supports a very large vocabulary of gesture templates in a lexicon, including practically all words needed for a particular user. In addition, the system utilizes various techniques and methods to achieve reliable recognition of a very large gesture vocabulary. Further, the system provides feedback and display methods to help the user effectively use and learn shorthand gestures for words. Word patterns are recognized independent of gesture scale and location. The present system uses language rules to recognize and connect suffixes with a preceding word, allowing users to break complex words into easily remembered segments.

A handwritten Chinese character input method and system is provided to allow users to enter Chinese characters to a data processor by adding less than three strokes and one selection movement such as mouse clicking or stylus or finger tapping. The system is interactive, predictive, and intuitive to use. By adding one or two strokes which are used to start writing a Chinese character, or in some case even no strokes are needed, users can find a desired character from a list of characters. The list is context sensitive. It varies depending on the prior character entered. Compared to other existing systems, this system can save users considerable time and efforts to entering handwritten characters.

Many portable electronic devices are designed to utilize only a touch-screen for text input, generally using some form of stylus to contact the screen. Such devices generally input text using some form of handwriting recognition, which is slow and often inaccurate, or an on-screen keyboard, which essentially requires the user to perform ''one-finger'' typing, often on a reduced-size keyboard. The Continuous Stroke Word-Based Text Input System allows someone to use a small on-screen keyboard to quickly enter words by drawing a continuous line that passes through or near the keys of each letter in a word in sequence without lifting the stylus (similar to a children's connect-the-dots drawing). The user traces an input pattern for a word by contacting the keyboard on or near the key of the first letter of the word, then tracing through each letter in sequence, lifting the stylus from the screen upon reaching the last letter. In one preferred embodiment, the user traces a small circle around each double-letter that occurs in the word to reduce ambiguity. In another preferred embodiment, a database of words is organized according to the first and last letters so that only a small number of words need to be explicitly scored for each input pattern. In another preferred embodiment, the expected path length corresponding to each word is stored in the database and is compared to the actual input path length entered to further limit the number of words to be explicitly scored. The input pattern is analyzed to identify inflection points of various types, each of which has a greater or lesser probability of corresponding to a letter of the word being input. Words are scored according to the average distance from each letter to the nearest inflection point (or to the nearest point of the traced line if there are more letters in the word than detected inflection points in the input pattern).

Ink strokes of cursive writing are segmented to make the cursive writing more like print writing, particularly with respect to the number of strokes of a character. A stroke-segmentation module first finds the local extrema points on a stroke of input ink. Then the local extrema points are stepped through, two (or three) at a time. The stroke-segmentation module may compare the three (or four) ink segments that are adjacent to the two (or three) local extrema points to a set of predefined stroke-segmentation patterns to find a closest matching pattern. Strokes are then segmented based on a stroke-segmentation rule that corresponds to the closest matching pattern. Additional stroke segmentation may be performed based on the change of curvature of the segmented ink strokes. Then, a character-recognition module performs character recognition processing by comparing the segmented ink strokes to prototype samples at least some of which have been similarly segmented.