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This paper proposes a lattice-based method for keyword spotting in online Chinese handwriting to improve the trade-off between accuracy and speed, and to overcome the out-of-vocabulary (OOV) problem of lexicon-driven approach. Using a character string recognition algorithm, the lattice-based method generates a
This paper presents a revised method for keyword search from Japanese handwritten digital ink. We employ Japanese string recognition and produce a candidate lattice. We search for a given keyword into the lattice so that we can search for the keyword even if constituent characters are not in the top candidates. We
Development of Optical Character Recognition (OCR) for printed Roman script is still an active area of research. Automatic Style Identification (ASI) can be used to improve the performance of OCR system and keyword spotting techniques for printed Roman script. This paper proposes a two stage font invariant technique
a text database using the filtered results. We further conduct a cache-based adaptation method on the resulting language model, in which keywords in the filtered results are cached and used to boost the word probability. In an experimental evaluation over real lectures, we obtained a significant improvement of ASR
recognition application, the technique show a very promising result with average 95% accuracy. Keywords: feature extraction, structural feature, statistical feature, pattern recognition, character recognition.
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