T12: an advanced text input system with phonetic support for mobile devices (original) (raw)

Less-Tap: A fast and easy-to-learn text input technique for phones

2003

A new technique to enter text using a mobile phone keypad, Less-Tap, is described. The traditional touchtone phone keypad is ambiguous for text input because each button encodes 3 or 4 letters. As in Multitap, our method requires the user to press buttons repeatedly to get a required letter. However, in Less-Tap, letters are rearranged within each button according to their frequency. This way, the most common letters require only one key press. Unlike dictionary based methods, Less-Tap facilitates the entry of arbitrary words. Unlike LetterWise and T9 ® , Less-Tap allows entering text without having to visually verify the result, after some initial training. For English, Less-Tap requires an average of 1.5266 keystrokes per character (vs. 2.0342 in Multitap).

Optimizing predictive text entry for short message service on mobile phones1

2000

Mobile phone based SMS messaging is a ubiquitous form of communication in the modern world. However, the 12- key keypad found on many mobile phones today poses problems for text entry. As three or four letters share the same key, some form of disambiguation is required to determine which letter is intended by the user. The predictive text entry method

Predicting text entry speed on mobile phones

2000

We present a model for predicting expert text entry rates for several input methods on a 12-key mobile phone keypad. The model includes a movement component based on Fitts' law and a linguistic component based on digraph, or letter-pair, probabilities. Predictions are provided for one-handed thumb and two-handed index finger input. For the traditional multi-press method or the lesser-used twokey method, predicted expert rates vary from about 21 to 27 words per minute (wpm). The relatively new T9 method works with a disambiguating algorithm and inputs each character with a single key press. Predicted expert rates vary from 41 wpm for one-handed thumb input to 46 wpm for two-handed index finger input. These figures are degraded somewhat depending on the user's strategy in coping with less-than-perfect disambiguation. Analyses of these strategies are presented.

KidSpell: A Child-Oriented, Rule-Based, Phonetic Spellchecker

2020

For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess the effectiveness of, we compare the model’s performance against several popular, mainstream spellcheckers in a number of offline experiments using existing and novel datasets. The results of these experiments show that KidSpell outperforms existing spellcheckers, as it accurately prioritizes r...

Tigrigna language spellchecker and correction system for mobile phone devices

International Journal of Electrical and Computer Engineering (IJECE), 2021

This paper presents on the implementation of spellchecker and corrector system in mobile phone devices, such as a smartphone for the low-resourced Tigrigna language. Designing and developing a spell checking for Tigrigna language is a challenging task. Tigrigna script has more than 32 base letters with seven vowels each. Every first letter has six suffixes. Word formation in Tigrigna depends mainly on root-and-pattern morphology and exhibits prefixes, suffixes, and infixes. A few project have been done on Tigrigna spellchecker on desktop application and the nature of Ethiopic characters. However, in this work we have proposed a systems modeling for Tigrigna language spellchecker, detecting and correction: A corpus of 430,379 Tigrigna words has been used. To indication the validity of the spellchecker and corrector model and algorithm designed, a prototype is developed. The experiment is tested and accuracy of the prototype for Tigrigna spellchecker and correction system for mobile phone devices achieved 92%. This experiment result shows clearly that the system model is efficient in spellchecking and correcting relevant suggested correct words and reduces the misspelled input words for writing Tigrigna words on mobile phone devices.