Locality Sensitive Pseudo-Code for Document Images

Terasawa, Kengo; Tanaka, Yuzuru

doi:10.1109/icdar.2007.4378678

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…The experiment proves that the enhanced version is more efficient, while the clustering results are similar to ones of original version. In [13], a LSH based scheme is proposed for representing character string images in the scanned document. Also, it is efficient without losing accuracy.…”

Section: Related Workmentioning

confidence: 99%

Locality Sensitive Hashing Based Service Classification

Wang

2011

2011 International Conference on Management and Service Science

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Technology of Web Service Classification can be widely used in the area of service applications. With the rapid growth of services, service classification turn to work in a poor performance. In this paper, we propose an efficient algorithm based on Locality Sensitive Hashing (LSH) to solve the service classification problem. This method is capable of high dimensional service classification as well. It is introduced that how to convert WSDL to vectors based on VSM. Then services are classified using LSH. We evaluate our approach and KNN method through experiment on different datasets. Two standards precision and recall are adopted to check the quality of service classification. The experiments have shown that our algorithm has a high performance and works stably.

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Section: Related Workmentioning

confidence: 99%

Locality Sensitive Hashing Based Service Classification

Wang

2011

2011 International Conference on Management and Service Science

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“…Then, the extracted image word candidates are represented by a feature descriptor. Different types of descriptors have been proposed [1], [2], [3], [4]. Some methods describe the word image with a global representation, e.g., gradient, contextual, and convexity features [5], [6], [7], [8], features based on moments of binary images [9] or features based on the spatial distribution of shape pixels in a set of predefined image sub-regions [10].…”

Section: Introductionmentioning

confidence: 99%

Sequential Word Spotting in Historical Handwritten Documents

Fernandez-Mota

Manmatha

Fornés

et al. 2014

2014 11th IAPR International Workshop on Document Analysis Systems

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In this work we present a handwritten word spotting approach that takes advantage of the a priori known order of appearance of the query words. Given an ordered sequence of query word instances, the proposed approach performs a sequence alignment with the words in the target collection. Although the alignment is quite sparse, i.e. the number of words in the database is higher than the query set, the improvement in the overall performance is sensitively higher than isolated word spotting. As application dataset, we use a collection of handwritten marriage licenses taking advantage of the ordered index pages of family names.

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“…The purpose of their worls is to extract and recognize words using description based on computed features. Terasawa et al [11] also performed word spotting inspired by an Eigen space method [11] or gradient [12]. In this case, word signatures are extracted from sliding windows.…”

Section: Related Workmentioning

confidence: 99%

Local Orientation Extraction for Wordspotting in Syriac Manuscripts

Bilane

Bres

Emptoz

2008

Lecture Notes in Computer Science

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Abstract. This paper presents a contribution to Word Spotting applied for digitized Syriac manuscripts. The Syriac language was wrongfully accused of being a dead language and has been set aside by the domain of handwriting recognition. Yet it is a very fascinating handwriting that combines the word structure and calligraphy of the Arabic handwriting with the particularity of being intentionally written tilted by an angle of approximately 45°. For the spotting process, we developed a method that should find all occurrences of a certain query word image, based on a selective sliding window technique, from which we extract directional features and afterwards perform a matching using Euclidean distance correspondence between features. The proposed method does not require any prior information, and does not depend of a word to character segmentation algorithm which would be extremely complex to realize due to the tilted nature of the handwriting.

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Locality Sensitive Pseudo-Code for Document Images

Abstract: In this paper, we propose a novel scheme for representing

Cited by 16 publications

References 15 publications

Locality Sensitive Hashing Based Service Classification

Locality Sensitive Hashing Based Service Classification

Sequential Word Spotting in Historical Handwritten Documents

Local Orientation Extraction for Wordspotting in Syriac Manuscripts

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