Kritawan Siriboon

HMM topology selection for on-line Thai handwriting recognition

Researchers have extensively applied hidden Markov models (HMM) to handwriting recognition in English, Chinese, and other languages. Most researchers have used left-right topology for handwriting and speech recognition. This research studied the effect of HMM topology on isolated online Thai handwriting recognition. The left-right, fully connected and proposed topologies (left-right-left) were compared. The number of states of a character HMM for each topology was varied from 15 to 35 nodes and the one with the best training observations probability was selected. The feature used was chain code-like with modifications to represent original quadrant position. The recognition results showed that the proposed topology increases the recognition rate compared to the most widely used left-right topology.

State Machine Induction with Positive and Negative Training for Thai Character Recognition

One problem of generating a model to recognize any string is how to generate one that is generalized enough to accept strings with similar patterns and, at the same time, is specific enough to reject the non-target strings. This research focus on generating a model in the form of a state machine to recognize strings derived from the direction information of characters images. The state machine induction process has two steps. The first step is to generate the machine from the strings of each target character (positive training), and the second step is to adjust the machine to reject any other string (negative training). This state machine induction method that automatically learns from strings can be applied with other string patterns recognition apart from characters.

Thai OCR error correction using genetic algorithm

This paper presents an efficient method for Thai OCR error correction based on genetic algorithm (GA). The correction process starts with word graph construction from spell checking with dictionary, then a graph is searched for a corrected sentence with the highest perplexity (using language model, bi-gram and tri-gram) and word probability from OCR. For a long sentence, a search space is huge and can be resolved using GA. A list of nodes is used for chromosome encoding to represent all possible paths in a graph instead of standard binary string. The performance of the suggested technique is evaluated and compared to the full search for tested sentences of different size constructed from 10 nodes to 200 nodes word graphs.

A modified multi-swarm optimization with interchange GBEST and particle redistribution

The Particle Swarm Optimization (PSO) is an optimization algorithm using multiples particle to search solution space for an optimize solution. Each particle of PSO moves toward the best solution within its group. For this behavior, PSO often traps in local optimum. Many researchers proposed splitting a swarm into multiple swarms so that they may move to different local optimum. Besides, the mutation operation technique, the natural selection technique and the crossover operation technique are added to normal PSO process. These proposed techniques are called Selective Crossover base on Fitness in Multi-Swarm Optimization (SFMPSO) and Fast Multi-swarm Optimization (FMPSO). However, both techniques used too many evaluation calls dues to crossover and the mutation operation. This paper proposes setting the best position (GBEST) of a trapped swarm to GBEST of the other swarm. Then, the swarms particle is redistributed in solution space before restart the trapped swarm. This proposed technique is evaluated on a set of twenty-six benchmark test functions. The experimental results show that the results are better than those of PSO, FMPSO and SFMPSO.