A Survey on Thai Input Methods on Smartphones <s> Discussion <s> This paper introduces an approach for improving Thai text inputting via virtual keyboard on touch screen mobile phones. We propose a technique for character candidate selection to choose the most proper character sequence. The proposed approach consists of two main parts i.e. candidate character generation based on touch area, and trigram model for candidate selection. The candidate generation is used to acquire more than one character per one touch. We define the area nearby the touch point as a touch area. The area is defined based on the statistics collected from various users. The characters within the touch area are considered as a set of candidate characters. The candidates are weighted based on their distances from the touch point. Then, the character trigram model is applied to select the combination of characters with the highest probability suggested to user. In order to evaluate the performance of the proposed technique, we evaluate the word-level accuracy of the words generated by the proposed technique and the words generated by combining the characters with the shortest distances from the touch point. Our technique yields better performance. It produces the results with the accuracy of 93.29% in character level and 66.67% in word level, while the accuracy is 83.48% in character level and 48.33% in word level when the nearest characters are selected. <s> BIB001 </s> A Survey on Thai Input Methods on Smartphones <s> Discussion <s> This paper introduces an approach to improve Thai text entry on virtual keyboards. We propose a word completion on new keyboard layouts called vowel-separated keyboard layouts. Based on a Thai writing system, all vowels locate only four positions i.e. top, bottom, left and right of a consonant. Thus, we design our keyboard layouts by grouping vowels with the same position together and place the vowel group buttons into four positions of keyboard area. This should reduce the searching time when typing vowels. While all consonants still contain in the middle area and the consonant's arrangement is based on standard Thai layout named Kedmanee layout. We design two type of the vowel-separated layouts i.e. distance-based layout and big-button layout. We compare the performance of the proposed layout with the ordinary layout. Both proposed layouts yield better performance than the ordinary one especially the big-button layout. Most of test subjects prefer to use big-button layout because the layout look more simple and they can easily find the character they want. <s> BIB002 </s> A Survey on Thai Input Methods on Smartphones <s> Discussion <s> This paper presents an approach to improve the correctness for Thai text inputting via virtual keyboard on touch screen mobile phones. The proposed approach is to generate candidate character based on statistical model from bivariate analysis of pre-collected coordinate data and apply the character trigram model to each candidate character sequence. From user's touch positions, a set of candidate characters with high position-based probability is generated. Then, the character trigram model is applied to each generated candidate characters sequence. For each character sequence, a probability is computed from the weighted combination of position-based and character trigram models. In the end, the character sequence with the highest probability is selected to be the most appropriate sequence. Experiments were conducted to compare the typing accuracy between an ordinary Thai virtual keyboard and our proposed algorithm using the same Thai keyboard layout. Results demonstrate that the proposed algorithm provides the improvement in the text entry accuracy in both character levels and word levels. <s> BIB003 </s> A Survey on Thai Input Methods on Smartphones <s> Discussion <s> The basic concepts and initial prototype of a word-gesture keyboard are discussed. In early 1980's, Montgomery conceived the idea of using sliding gestures on a touch keyboard to enter characters. He designed a wipe activated keyboard with a flat touch sensitive surface. The positions of the letter keys were carefully arranged to make consecutive letters commonly appear in words connected on the keyboard. Since a gesture keyboard enhances, rather than replaces, a conventional touchscreen keyboard, out of vocabulary (OOV) letter sequences can always be entered by typing the individual letter keys. In using a word-gesture keyboard, the production of movements increasingly changes from focusing on individual letters to connecting multiple letters into a word gesture. Conceptually, gesture recognition is done by identifying the word which has the highest probability given the user's gesture. Exact or statistical modeling of gesture keyboard's speed-accuracy trade-off incorporating human control behavior is under research. <s> BIB004
The Button Size Adjustment, used in Keyboard ManMan  , technique provides a positive effect in terms of convenience when it predicts a high probability for the character the user intends to input. It may become a negative effect when the user wants to input an uncommon or unknown word. The presented Distance-based Candidate Generation and Bivariate Normal Distribution-based Candidate Generation, presented in BIB001 and BIB003 , techniques generate multiple character candidates from a touch point. They increase the chance that the intended character is chosen as one of the candidates. However, too many candidates may confuse the user. The models for generating character candidates require more detailed study to provide a higher accuracy. The candidate generation using sliding input, used in Swype  and TSwipe-Pro  , allows faster word input since it does not require up and down movements. Moreover, one input method can be developed to support both sliding and touch inputs. This sliding input technique is a promising method for future development as suggested in BIB004 . Generating word candidates using a word list, used in BIB002 ,  , is able to construct a list of meaningful words for users. It reduces the number of touchs required to complete a word. However, this technique cannot deal well with unknown words such as proper names. Generating word candidates from consonant only  ,  reduces the number of buttons on the layout. The technique has a severe disadvantage that the user cannot input any unknown word. Generating word candidates using a statistical language model (e.g. n-gram model) BIB003 provides an advantage on generating unknown word candidates. Nevertheless, the candidates may not be meaningful words. Combining both techniques will utilize the advantages from both of them. Moreover, a language model of meaningful unknown words should be studied in the future in order to generate more appropriate word candidates. This can be done by analyzing the structure of Thai proper nouns. Ranking word candidates using word frequency from standard corpus, used in BIB002 - BIB003 , has a disadvantage that it is unable to deal with unknown words. Utilizing weighted word frequency from user word list, used in  , provides an advantage on dealing with both known and unknown words since the user is allowed to define his/her own words. Edit distance  can also be applied with the other two techniques in order to emphasize inputting the exact string. To improve the performance of the candidate ranking, the statistical data should reflect the actual word usage on smartphones. However, all the Thai standard corpora are now collected from written documents. The statistics obtained those corpora may not be a good representation of the conversational language typically used in smartphones. A corpus from informal language usage is necessary to improve the candidate ranking. The adaptive bivariate normal distribution technique BIB003 adjusts the bivariate parameters when the user selects one of the candidates. Utilizing feedback from the user will allow an input method to adjust the models to suit the user's characteristics. However, the process needs to be done without disturbing the usability of the input method. Moreover, it should be able to efficiently handle errors that may cause from the user, for example, updating the models when the user chose an unintended word from the candidates. This is still a challenge in the user interface design.