Sanparith Marukatat

Online Handwritten Shape Recognition Using Segmental Hidden Markov Models

We investigate a new approach for online handwritten shape recognition. Interesting features of this approach include learning without manual tuning, learning from very few training samples, incremental learning of characters, and adaptation to the user-specific needs. The proposed system can deal with two-dimensional graphical shapes such as Latin and Asian characters, command gestures, symbols, small drawings, and geometric shapes. It can be used as a building block for a series of recognition tasks with many applications

Rejection measures for handwriting sentence recognition

In this paper we study the use of confidence measures for an on-line handwriting recognizer. We investigate various confidence measures and their integration in an isolated word recognition system as well as in a sentence recognition system. In isolated word recognition tasks, the rejection mechanism is designed in order to reject the outputs of the recognizer that are possibly wrong, which is the case for badly written words, out-of-vocabulary words or general drawing. In sentence recognition tasks, the rejection mechanism allows rejecting parts of the decoded sentence.

Sentence recognition through hybrid neuro-Markovian modeling

This paper focuses on designing a handwriting recognition system dealing with on-line signal, i.e. temporal handwriting signal captured through an electronic pen or a digitalized tablet. We present here some new results concerning a hybrid on-line handwriting recognition system based on Hidden Markov Models (HMMs) and Neural Networks (NNs), which has already been presented in several contributions. In our approach, a letter-model is a Left-Right HMM, whose emission probability densities are approximated with mixtures of predictive multilayer perceptrons. The basic letter models are cascaded in order to build models for words and sentences. At the word level, recognition is performed thanks to a dictionary organized with a tree-structure. At the sentence level, a word-predecessor conditioned frame synchronous beam search algorithm allows to perform simultaneously segmentation into words and word recognition. It processes through the building of a word graph from which a set of candidate sentences may be extracted. Word and sentence recognition performances are evaluated on parts of the UNIPEN international database.

A study on instance-based learning with reduced training prototypes for device-context-independent activity recognition on a mobile phone

This paper presents a study of two simple methods for reducing the complexity of the instance-based classification technique and demonstrates their use in device-context independent activity recognition on a mobile phone. A projection-based method for signal rectification has been implemented on an iPhone in order to handle with variation in device orientations. The transformation matrix is estimated on a ten-second dynamic data buffer. To search for a suitable set of training prototypes for iPhone implementation, an activity recognition experiment is conducted with twenty different device contexts performed by eight subjects. With the developed mobile application, the recognition results along with the users location can be displayed on both iPhone and the web application in real time.

Handling spatial information in on-line handwriting recognition

This paper focuses on handling the two-dimensional feature of on-line handwriting signals in recognition engines. This spatial information is taken into account in various ways depending on the nature of characters to be recognized. We review some techniques used in the literature and investigate new ones to represent and model the spatial information in handwriting recognition engines. We compare formally and experimentally a number of solutions on various character recognition tasks.

A generic approach for on-line handwriting recognition

We present here thorough experimental studies of a generic approach for developing on-line handwriting recognition systems. The basic principles of our approach lead to generic intrinsic properties that we investigate. These properties allow building various recognition engines corresponding to different needs in pen-based interaction.

The feature combination technique for off-line Thai character recognition system

This paper proposes an off-line Thai character recognition system for an unconstrained writing. This system combines both global feature, that represents character shape, and local feature, that represents the symbolic structure, to solve the similarity and variety problems of Thai characters. The pixel distribution is applied, as the global feature, to classify the image into 20 groups of similar characters. The modified structure features extraction is used to extract character structure. Then, all features, global and local, are used to classify the character within each group. The proposed method yields the recognition rate at 87.32 per cents.

A method for shoulder range-of-motion estimation using a single wireless sensor node

This study proposes a method for range-of-motion (ROM) estimation based on the acceleration and geomagnetic data acquired using a single miniaturized wireless sensor node. An experiment on eight shoulder rehabilitation protocols in real human subjects has been conducted, with a sensor placed on users left and right upper arms and wrists. The experimental results demonstrate the limitations of estimation methods that use sensors placed on skin surface and that, despite being a different body segment, the wrist is a better placement position for sensor-based shoulder joint ROM measurement than the shoulder itself.