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For text-query-based keyword spotting from handwritten Chinese documents, the index is usually organized as a candidate lattice to overcome the ambiguity of character segmentation. Each edge in the lattice denotes a candidate character associated with a candidate class. Character similarity (between character and
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
lower out of vocabulary rates. This paper proposes a morph-to-word transduction to convert morph sequences into word sequences. This enables powerful word language models to be applied. In addition, it is expected that techniques such as pruning, confusion network decoding, keyword search and many others may benefit from
In this work, keyword search (KWS) is based on a symbolic index that uses posteriorgram representation of the speech data. For each query, sum-to-one normalization or keyword specific thresholding is applied to the search results. The effect of these methods on the proposed KWS system is investigated. Results are
additional ordering present in a lattice or CN is discarded. TMW lists compactly summarize a large ASR search space. Representing a large search space is critical for STD metrics such as ATWV that heavily penalize misses of rare keywords. Comparisons on the OpenKWS 2014 Tamil limited language pack task [1] show that the new TMW
generally have problems on keyword-search problem. In this paper, we proposed an initial model to solve the problem by using Case-Based Reasoning (CBR) and Formal Concept Analysis (FCA). For the proposed model, a case base is created to represent design patterns. FCA is used to be case organization that analyze case base for
Software developers currently find design patterns through search tools for solving software design problem. However, these search tools still have keyword-search problem. In this paper, we introduce the elementary idea to improve the design pattern retrieval tool. We propose the combination of case based reasoning
Spoken term detection, especially of out-of-vocabulary (OOV) keywords, benefits from the use of sub-word systems. We experiment with different language-independent approaches to sub-word unit generation, generating both syllable-like and morpheme-like units, and demonstrate how the performance of syllable-like units
for different users. Our mechanism first constructs the relationship between the keywords and data files based on a Galois connection. And then we exploit data retrieval indexes with variable threshold, where granular data retrieval service can be supported by adjusting the threshold for different users. Moreover, to
of Amdahl theoretical thresholds. Rainbow options have been valued with several multivariate lattices models programmed according to our parallelization algorithms providing results not found in extant literature in terms of granularity and convergence. Keywords: Rainbow Options, multivariate binomial lattices
mis-recognition of sub-units. To solve the problem of OOV keywords and mis-recognized words, we used individual syllables as sub-word unit in continuous speech recognition and an n-gram sequence of syllables as a retrieval unit. We propose an n-gram indexing/retrieval method with distance in a syllable lattice for
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