Dictionary based disambiguation (DBD) has been well- researched since the 1970s. SHRIMP can minimize the concurrent movement requirement of Vision TiltText when entering in-dictionary words but leverage vision TiltText when entering OOV words. In this paper, we present SHRIMP that combines Vision TiltText with DBD. However, moving a cell phone left and right for entering about 60% of characters is an overhead which can be further reduced. With the help of a concurrent gesture movement, Vision TiltText can achieve 1 KSPC on any character. Implementation details can be found in and. Instead of using an accelerometer, Vision TiltText uses the built-in camera on a phone to detect motion so that it can run on unmodified mainstream camera phones. Vision TiltText by Wang et al is a remake of the TiltText input method by Wigdor and Balakrishnan. MultiTap is simple, unambiguous but tedious. It requires the user to press the key labeled with the desired character repeatedly until the correct character appears on the screen. MultiTap is perhaps the most popular text entry method for mobile phones. SHRIMP uses seamlessly integrated concurrent motion gestures to handle ambiguous dictionary words or OOV words without mode switching. SHRIMP is as effective as conventional DBD when entering unambiguous in-dictionary words. SHRIMP is a predictive text input method based on Vision TiltText and runs on camera phones equipped with a standard 12- button keypad. In this paper, we present a novel method called SHRIMP 1 (Small Handheld Rapid Input with Motion and Prediction) which enable the user to handle the collision and OOV problems of DBD input more easily. Character level n-gram model based disambiguation, such as LetterWise, can achieve a KSPC that is close to DBD and works for OOV words however, continuous visual attention is required to confirm suggested characters after each key press. However, they depend on an alternative input method to enter words that are not in the dictionary known as out- of-vocabulary (OOV) words and suffer from the encoding collision problem (to be detailed in the next section). DBD input techniques such as T9 can achieve approximately 1.007 KSPC (Key Stroke Per Character) for words that are in the dictionary. Action based disambiguation allows users to enter any character deterministically, but requires additional sequential or concurrent actions. Methods in both categories have their unique strengths and weaknesses. Besides disambiguating uncertain input strings, linguistic knowledge can also be used for predicting users’ future intention (a.k.a.
T9 ), character level N-gram models (e.g.
Linguistic knowledge can be leveraged either through Dictionary-based Disambiguation (DBD e.g. Also known as predictive input, these methods use redundant information in language to disambiguate users’ input when entering standard English words. MultiTap, TNT ), concurrent chording, tilting or motion to select one character from the multiple alphabetical characters on each key. Most disambiguation methods can be categorized into the following two categories: Action Based Disambiguation. All mobile text input techniques relying on the 12-key keypad have to resolve the ambiguity that arises from this one-to-many mapping.
In the ITU E.161 standard, one numeric button corresponds to 3 or 4 alphabet characters on the keypad ( Figure 1). One fundamental difficulty in text entry using a 12-key keypad is that the mapping of 26 alphabet characters to the 12 keys is inherently ambiguous. Other input devices, such as mini QWERTY keyboards and touch screens are on the rise, but the 12-button keypad-based mobile phones are still, and will likely to be for years to come, the majority in the market. This keypad is effective for dialing phone numbers but not for editing contact lists, composing SMS messages or writing emails. Due to trade-offs in both size and compatibility, most mobile phones today are equipped with a 12-key keypad ( Figure 1). However, despite the rapid growth of mobile phones, text entry on small devices remains a major challenge. Their portability and communication capabilities have revolutionized how people interact with each other. an estimated 4 billion units in use in December 2008, mobile phones have already become the most popular computing device in human history.
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