A Search Method for On-Line Handwritten Text Employing Writing-Box-Free Handwriting Recognition

Oda, Hideto; Kitadai, Akihito; Onuma, Motoki; Nakagawa, Masaki

doi:10.1109/iwfhr.2004.9

Cited by 17 publications

(5 citation statements)

References 2 publications

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“…Therefore, Table 6 implies that the most important feature function is the character classifier feature function f 6 , then the next important feature functions are the inner gap feature function f 3 and the segmentation feature function f 5 .…”

Section: Resultsmentioning

confidence: 99%

“…Their proposed approach used handwriting synthesis to do matching in the ink domain as opposed to the use of a classifier [4]. Oda et al proposed a search system for finding keywords in digital ink by employing ONHCR [5]. Zhang et al employed a one-vs-all (OVA) trained character classifier for keyword searches from online handwritten Chinese documents [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digital Ink Search Based on Character-Recognition Candidates Compared with Feature-Matching-Based Approach

Cheng

Zhu

Nakagawa

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents an approach based on character recognition to searching for keywords in on-line handwritten Japanese text. It employs an on-line character classifier and an off-line classifier or a combined classifier, which produce recognition candidates, and it searches for keywords in the lattice of candidates. It integrates scores to individually recognize characters and their geometric context. We use quadratic discriminant function(QDF) or support vector machines(SVM) models to evaluate the geometric features of individual characters and the relationships between characters. This paper also presents an approach based on feature matching that employs on-line or off-line features. We evaluate three recognition-based methods, two feature-matching-based methods, as well as ideal cases of the latter and concluded that the approach based on character recognition outperformed that based on feature matching.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital Ink Search Based on Character-Recognition Candidates Compared with Feature-Matching-Based Approach

Cheng

Zhu

Nakagawa

2013

IEICE Trans. Inf. & Syst.

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“…36,38,45 These methods are similar to ours, but we have paid more attention to the training of character classi¯er for a better similarity measure, which has been shown to be an important issue. 1,9,42 In this work, we show the superiority of the OVA trained prototype classi¯er over the linear support vector machine (SVM) and a prototype classi¯er trained with conventional multi-class objective.…”

Section: Introductionmentioning

confidence: 95%

Keyword Spotting From Online Chinese Handwritten Documents Using One-Versus-All Character Classification Model

Zhang

Wang

Liu

et al. 2013

Int. J. Patt. Recogn. Artif. Intell.

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In this paper, we propose a method for text-query-based keyword spotting from online Chinese handwritten documents using character classi¯cation model. The similarity between the query word and handwriting is obtained by combining the character classi¯cation scores. The classi¯er is trained by one-versus-all strategy so that it gives high similarity to the target class and low scores to the others. Using character classi¯cation-based word similarity also helps overcome the out-of-vocabulary (OOV) problem. We use a character-synchronous dynamic search algorithm to e±ciently spot the query word in large database. The retrieval performance is further improved by using competing character confusion and writer-adaptive thresholds. Our experimental results on a large handwriting database CASIA-OLHWDB justify the superiority of oneversus-all trained classi¯ers and the bene¯ts of con¯dence transformation, character confusion and adaptive thresholds. Particularly, a one-versus-all trained prototype classi¯er performs as well as a linear support vector machine (SVM) classi¯er, but consumes much less storage of index¯le. The experimental comparison with keyword spotting based on handwritten text recognition also demonstrates the e®ectiveness of the proposed method.

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“…Existing methods for handwriting search can be generally divided into recognition-based and recognition-free approaches. Most previous works with high search accuracy employ character recognition methods [1] [2]. Because the recognition engine obscures differences in writing style, these methods are tolerant of different writers.…”

Section: Introductionmentioning

confidence: 99%

Fast and Memory Efficient Online Handwritten Strokes Retrieval Using Binary Descriptor

Shibata

Tonouchi

Kubota

et al. 2013

2013 2nd IAPR Asian Conference on Pattern Recognition

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We propose a fast, memory-efficient online handwriting search method that uses handwritten strokes as a query and finds matches from among handwritten documents. The proposed method is language-independent, so not only words but also figures and symbols can be queried. We introduce a compact binary descriptor to lower computational resource load. A metric learning method enables derivation of a discriminative binary descriptor from directional densities of handwritten strokes. Experiments indicate that the proposed method is faster, more memory efficient, and exhibits more accurate search performance than a conventional method that employs directional densities. For 200 handwritten documents, the proposed method completed query searches within 1 s using a 1.3 GHz Tegra 3 CPU.

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A Search Method for On-Line Handwritten Text Employing Writing-Box-Free Handwriting Recognition

Cited by 17 publications

References 2 publications

Digital Ink Search Based on Character-Recognition Candidates Compared with Feature-Matching-Based Approach

Digital Ink Search Based on Character-Recognition Candidates Compared with Feature-Matching-Based Approach

Keyword Spotting From Online Chinese Handwritten Documents Using One-Versus-All Character Classification Model

Fast and Memory Efficient Online Handwritten Strokes Retrieval Using Binary Descriptor

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