Knowing varieties of writing a letter in a word or a subword in different handwriting styles is very beneficial in recognition specifically for online recognition. In this paper, TMU-OFS dataset consisting of 1000 frequent Farsi subwords is employed to study Farsi handwriting styles. The subwords are grouped based on their delayed strokes and their main bodies, separately. The handwriting style...

This paper takes handwriting-based human interfaces as human-centered and creative human interfaces and considers the directions of research and development on on-line handwriting recognition. Then, it summarizes our research on collection of sample pattern database, combination of on-line and off-line recognition methods, a model and implementation of format free handwriting recognition, segme...

Based on accelerometer, we propose a 3D handwriting recognition system in this paper. The system is consists of 4 main parts: (1) data collection: a single tri-axis accelerometer is mounted on a handheld device to collect different handwriting data. A set of key patterns have to be written using the handheld device several times for consequential processing and training. (2) data preprocessing:...

This paper presents an extensive investigation of the use of trigraphs for on-line cursive handwriting recognition based on Hidden Markov Models (HMMs). Trigraphs are context dependent HMMs representing a single written character in its left and right context, similar to triphones in speech recognition. Looking at the great success of triphones in continuous speech recognition ([1]-[3]), it was...

The BYBLOS continuous speech recognition system is applied to on-line cursive handwriting recognition. By exploiting similarities between on-line cursive handwriting and continuous speech recognition, we can use the same base system adapted to handwriting feature vectors instead of speech. The use of hidden Markov models obviates the need for segmentation of the handwritten script sentences bef...

Handwriting recognition is a rich and complex issue. Some of its problems include the large shape variations in human handwriting. Classifier combination contributes in increasing the classification accuracy compared to the performance of individual classifier. In this paper, we present an online handwriting recognizer based on classifier combination according to holistic approach. We propose t...

Handwriting recognition is one of the most challenging tasks and exciting areas of research in computer vision. Numerous document recognition methods have been proposed in various languages and character set such as Arabic, India, Korean, Japanese, Chinese and so on. This paper presents the recent result of the research work of Myanmar handwriting text recognition and translation. Each segmente...

Handwriting extraction is the skill of a system to get and translate comprehensible hand written input via sources such as document, photos, tough screen and other devices. The picture of the written document is used to detect written text by the use of optical scanning i.e. known as optical character recognition. Handwriting extraction basically uses optical character recognition. Conversely, ...

This paper describes the method to recognize offline handwritten characters. A robust algorithm for handwriting segmentation is described here with the help of which individual characters can be segmented from a selected word from a paragraph of handwritten text image which is given as input.

A hidden Markov model (HMM) based continuous speech recognition system is applied to on-line cursive handwriting recognition. The base system is unmodified except for using handwriting feature vectors instead of speech. Due to inherent properties of HMMs, segmentation of the handwritten script sentences is unnecessary. A 1.1% word error rate is achieved for a 3050 word lexicon, 52 character, wr...