LSH-based large scale chinese calligraphic character recognition

Lin, Yuan; Wu, Jiangqin; Gao, Peng; Yang, Xin; Mao, Tianjiao

doi:10.1145/2467696.2467704

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…The GIST descriptor was initially proposed in [20] and has recently shown good results for calligraphic character image searching [10]. The idea is to develop a low dimensional representation of the scene, which does not require any form of segmentation.…”

Section: Normalization and Feature Extractionmentioning

confidence: 99%

“…In [2], contour shape context (CSC) descriptor was use to perform retrieval. We used the probability computing strategy (PCS) in [3] and [10] to perform recognition basing on the retrieval result using CSC.…”

Section: Sh-mqdf Recognition Evaluation On Different Stylesmentioning

confidence: 99%

“…So this similarity searching based method is not suitable for large scale calligraphic character recognition. Lin et al [10] introduced LSH (Locality-sensitive hashing) to solve the efficiency problem when dealing with large scale data. Both Yu and Lin's methods perform the recognition by calculating the probability of the candidates whose order is determined by the similarity searching.…”

Section: Introductionmentioning

confidence: 99%

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SH-MQDF based calligraphic character recognition

Gao

2014

2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

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Chinese Calligraphy draws a lot of attention for its beauty and elegance. Until now, lot of people don't know the sematic meaning of many calligraphic characters because they were written in ancient times. Technologies which can help users to recognize the unknown calligraphic characters are urgently required. However recognizing calligraphic characters is a challenging work due to the reasons: (1)complication. There are at least five different styles of the same character;(2)Deformation. Sometimes deliberate deformation in writing is performed, which makes the recognition even harder. In this paper, we present an approach for the recognition of calligraphic characters. Modified quadratic discriminant function (MQDF), which was the state-of-the-art classifier used in handwriting recognition, is introduced in recognizing calligraphic characters. Due to the high expenses of storage and computation of MQDF on large scale data, we use spectral hashing(SH) to dynamically reduce the whole training set to a much more relevant but much smaller set, called the approximate set. MQDF is then trained on the approximate set dynamically and classification is performed. We evaluated our approach on the calligraphic character dictionary (CCD), which is an expert labeled calligraphic character database. Our experiments show that our approach reduces the training time of classifier but doesn't lower the accuracies of recognition much. Furthermore, our approach outperforms the existing calligraphic character recognition methods on CCD.

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Section: Normalization and Feature Extractionmentioning

confidence: 99%

Section: Sh-mqdf Recognition Evaluation On Different Stylesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SH-MQDF based calligraphic character recognition

Gao

2014

2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

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“…Y. Lin et al used GIST descriptor to represent Chinese calligraphy characters in (Lin et al, 2013)to recognize large scale Chinese calligraphic characters.…”

Section: Introductionmentioning

confidence: 99%

Chinese Character Images Recognition and Its Application in Mobile Platform

Gu¹,

Mao

et al. 2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Chinese characters are profound and polysemantic. Reading a Chinese character is a procedure of image understanding, if the Chinese character is captured as an image. Due to the complexity of structure and plenty of Chinese characters, there always exist some unfamiliar characters when reading books, so it would be great if a tool is provided to help users understand the meaning of unknown characters. We propose a method that combines global and local features(i.e., GIST and SIFT features) to recognize the Chinese character images captured from mobile camera. Three schemes are investigated based on practical considerations. Firstly,the so-called GIST and SIFT descriptors extracted from Chinese character images are adopted purely as features. Then filter the SIFT feature points of similar Chinese character images based on GIST feature. Finally, compress the storage of GIST and SIFT descriptors to accommodate mobile platform with Similarity Sensitive Coding(SSC) algorithm. At the stage of recognition, the top 2k Chinese characters are firstly obtained by hamming distance in GIST feature space, then reorder the selected characters as final result by SIFT feature. We build an Android app that implements the recognition algorithm. Experiment shows satisfying recognition results of our proposed application compared to other Android apps.

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“…It has recently shown good results for image search [4]. Y. Lin [5] used GIST descriptor for calligraphy word recognition and achieved good experiment results.…”

mentioning

confidence: 99%

Calligraphy Word Style Recognition by KNN Based Feature Library Filtering

Mao¹,

Wu²,

Gao³

et al. 2013

Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)

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The large amount of Chinese calligraphy works in existence is a valuable part of the Chinese cultural heritage. With the development of the digital library, a lot of Pengcheng Gao Yang Xia Yuan Lin Abstr act. Chinese calligraphy works is a valuable part of the Chinese culture heritage. More and more calligraphy works are digitized, preserved and exhibited in digital library so that people can enjoy the calligraphers' works conveniently. There are five main writing style categories of calligraphy words, namely, seal script, clerical script, standard script, semi-cursive script and cursive script. Users always want to appreciate the style-similar works simultaneously, so it's necessary to classify the words by their writing style. In this study, we proposed a method based on KNN and feature vector filtering. Firstly, extract the SIFT points from the training images, building up a feature library for SIFT feature vectors. Then use a KNN-based method to filter the feature library so that the style-irrelevant feature points can be wiped out. At last we use the filtered feature library to classify the word images by a modified KNN classifier. Experiments show that SIFT feature has better recognition result than that of Gabor feature and GIST feature, but the large amount of feature vectors in the SIFT feature library makes the KNN searching rather slow. To accelerate the recognition speed, Spectral Hashing is used to index the feature library, which makes it faster to classify feature points and gives no side effect on the recognition ratio.

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LSH-based large scale chinese calligraphic character recognition

Cited by 10 publications

References 16 publications

SH-MQDF based calligraphic character recognition

SH-MQDF based calligraphic character recognition

Chinese Character Images Recognition and Its Application in Mobile Platform

Calligraphy Word Style Recognition by KNN Based Feature Library Filtering

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