Online Handwritten Lao Character Recognition by MRF

Abstract

SUMMARYThis paper describes on-line recognition of handwritten Lao characters by adopting Markov random field (MRF). The character set to recognize includes consonants, vowels and tone marks, 52 characters in total. It extracts feature points along the pen-tip trace from pen-down to pen-up, and then sets each feature point from an input pattern as a site and each state from a character class as a label. It recognizes an input pattern by using a linear-chain MRF model to assign labels to the sites of the input pattern. It employs the coordinates of feature points as unary features and the transitions of the coordinates between the neighboring feature points as binary features. An evaluation on the Lao character pattern database demonstrates the robustness of our proposed method with recognition rate of 92.41% and respectable recognition time of less than a second per character. key words: online handwritten character recognition, Lao character recognition, Markov random field

Construction of Alphabetic Character Recognition Systems: A Review

“…AbugidasIt is a writing script primarily based on a consonant letter and secondary vowel notation. They are sharing with alphabetic systems the property b. Southeast Asia: Thai, Lao, Burmese, Javanese, and Balinese, the languages of this group have achieved the highest validation rate where Thai, Lao, and Burmese attained 92.1%, 92.41%, and 96.4% while Javanese and Balinese gained 97.7% and 97.53%, respectively[40]-[43].…”