Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

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Online and off-line handwriting recognition: a comprehensive survey

An enhanced text entry system using word-level analysis to automatically correct inaccuracies in user keystroke entries on reduced keyboards. The keyboard (105) may be a part of a touch-sensitive panel or display screen (100) or on a mechanical keyboard system. A method and system are defined which determine one or more alternative textual interpretations of each sequence of inputs detected within a designated auto-correcting keyboard region (106). The actual contact locations for the keystrokes may occur outside the boundaries of the specific keyboard key regions, where the distance from each contact location to each corresponding intended character may in general increase with the expected frequency of the intended word in the language or in a particular context. The user can easily select the intended word from among the generated interpretations.

Keyboard system with automatic correction

There is disclosed an enhanced text entry system which uses word-level analysis to correct inaccuracies automatically in user keystroke entries on reduced-size or virtual keyboards. A method and system are defined which determine one or more alternate textual interpretations of each sequence of inputs detected within a designated auto-correcting region. The actual interaction locations for the keystrokes may occur outside the boundaries of the specific keyboard key regions associated with the actual characters of the word interpretations proposed or offered for selection, where the distance from each interaction location to each corresponding intended character may in general increase with the expected frequency of the intended word in the language or in a particular context. Likewise, in a virtual keyboard system, the keys actuated may differ from the keys actually associated with the letters of the word interpretations. Each such sequence corresponds to a complete word, and the user can easily select the intended word from among the generated interpretations. Additionally, when the system cannot identify a sufficient number of likely word interpretation candidates of the same length as the input sequence, candidates are identified whose initial letters correspond to a likely interpretation of the input sequence.

Virtual keyboard system with automatic correction

A system associated with a text entry application, such as email or instant messaging, comprises an optional onscreen representation of a circular keyboard, a list of potential linguistic object matches, and a message area where the selected words are entered. The circular keyboard is manipulated via a hardware joystick or game-pad with analog joystick or omni-directional rocker switch built therein. The user points the joystick in the general direction of the desired letter, and then continues pointing roughly to each letter in the desired word. Once all letters have been roughly selected, buttons are used to select a specific word from the list of potential matches and send the selected word to the message area.

Directional input system with automatic correction

A selective input system and associated method is provided which tracks the motion of a pointing device over a region or area. The pointing device can be a touchpad, a mouse, a pen, or any device capable of providing two or three-dimensional location. The region or area is preferably augmented with a printed or actual keyboard/pad. Alternatively, a representation of the location of the pointing device over a virtual keyboard/pad can be dynamically shown on an associated display. The system identifies selections of items or characters by detecting parameters of motion of the pointing device, such as length of motion, a change in direction, a change in velocity, and or a lack of motion at locations that correspond to features on the keyboard/pad. The input system is preferably coupled to a text disambiguation system such as a T9® or Sloppytype™ system, to improve the accuracy and usability of the input system.

Touch screen and graphical user interface

On-line handwritten alphanumeric character recognition using dominant points in strokes

In this paper we present an approach in which an on-line handwritten character is characterized by a sequence of dominant points in strokes and a sequence of writing directions between consecutive dominant points. The directional information is used for character preclassification and the positional information is used for fine classification. Doth preclassification and fine classification are based on dynamic programming matching. A recognition experiment has been conducted with 62 character classes of different writing styles and 21 people as data contributors. The recognition rate of this experiment is 91%, with 7.9% substitution rate and 1.1% rejection rate. The average processing time is 0.35 second per character on a 486 50MHz personal computer.

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Xiaolin Li

Papers

On-line handwritten alphanumeric character recognition using dominant points in strokes

On-Line Handwritten Alphanumeric Character Recognition Using Feature Sequences

On-line handwritten alphanumeric character recognition using feature sequences