The goal of document image quality assessment (DIQA) is to build a computational model which can predict the degree of degradation for document images. Based on the estimated quality scores, the immediate feedback can be provided by document processing and analysis systems, which helps to maintain, organize, recognize and retrieve the information from document images. Recently, the bag-of-visual-words (BoV) based approaches have gained increasing attention from researchers to fulfill the task of quality assessment, but how to use BoV to represent images more accurately is still a challenging problem. In this paper, we propose to utilize a sparse representation based method to estimate document image's quality with respect to the OCR capability. Unlike the conventional sparse representation approaches, we introduce the target quality scores into the training phase of sparse representation. The proposed method improves the discriminability of the system and ensures the obtained codebook is more suitable for our assessment task. The experimental results on a public dataset show that the proposed method outperforms other hand-crafted and BoV based DIQA approaches.
{"title":"Document Image Quality Assessment Using Discriminative Sparse Representation","authors":"Xujun Peng, Huaigu Cao, P. Natarajan","doi":"10.1109/DAS.2016.24","DOIUrl":"https://doi.org/10.1109/DAS.2016.24","url":null,"abstract":"The goal of document image quality assessment (DIQA) is to build a computational model which can predict the degree of degradation for document images. Based on the estimated quality scores, the immediate feedback can be provided by document processing and analysis systems, which helps to maintain, organize, recognize and retrieve the information from document images. Recently, the bag-of-visual-words (BoV) based approaches have gained increasing attention from researchers to fulfill the task of quality assessment, but how to use BoV to represent images more accurately is still a challenging problem. In this paper, we propose to utilize a sparse representation based method to estimate document image's quality with respect to the OCR capability. Unlike the conventional sparse representation approaches, we introduce the target quality scores into the training phase of sparse representation. The proposed method improves the discriminability of the system and ensures the obtained codebook is more suitable for our assessment task. The experimental results on a public dataset show that the proposed method outperforms other hand-crafted and BoV based DIQA approaches.","PeriodicalId":197359,"journal":{"name":"2016 12th IAPR Workshop on Document Analysis Systems (DAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130758532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Offline handwriting recognition systems often include a decoding step, that is retrieving the most likely character sequence from the underlying machine learning algorithm. Decoding is sensitive to ranges of weakly predicted characters, caused e.g. by obstructions in the scanned document. We present a new algorithm for robust decoding of handwriting recognizer outputs using character n-grams. Multidimensional hierarchical subsampling artificial neural networks with Long-Short-Term-Memory cells have been successfully applied to offline handwriting recognition. Output activations from such networks, trained with Connectionist Temporal Classification, can be decoded with several different algorithms in order to retrieve the most likely literal string that it represents. We present a new algorithm for decoding the network output while restricting the possible strings to a large lexicon. The index used for this work is an n-gram index with tri-grams used for experimental comparisons. N-grams are extracted from the network output using a backtracking algorithm and each n-gram assigned a mean probability. The decoding result is obtained by intersecting the n-gram hit lists while calculating the total probability for each matched lexicon entry. We conclude with an experimental comparison of different decoding algorithms on a large lexicon.
{"title":"Increasing Robustness of Handwriting Recognition Using Character N-Gram Decoding on Large Lexica","authors":"M. Schall, M. Schambach, M. Franz","doi":"10.1109/DAS.2016.43","DOIUrl":"https://doi.org/10.1109/DAS.2016.43","url":null,"abstract":"Offline handwriting recognition systems often include a decoding step, that is retrieving the most likely character sequence from the underlying machine learning algorithm. Decoding is sensitive to ranges of weakly predicted characters, caused e.g. by obstructions in the scanned document. We present a new algorithm for robust decoding of handwriting recognizer outputs using character n-grams. Multidimensional hierarchical subsampling artificial neural networks with Long-Short-Term-Memory cells have been successfully applied to offline handwriting recognition. Output activations from such networks, trained with Connectionist Temporal Classification, can be decoded with several different algorithms in order to retrieve the most likely literal string that it represents. We present a new algorithm for decoding the network output while restricting the possible strings to a large lexicon. The index used for this work is an n-gram index with tri-grams used for experimental comparisons. N-grams are extracted from the network output using a backtracking algorithm and each n-gram assigned a mean probability. The decoding result is obtained by intersecting the n-gram hit lists while calculating the total probability for each matched lexicon entry. We conclude with an experimental comparison of different decoding algorithms on a large lexicon.","PeriodicalId":197359,"journal":{"name":"2016 12th IAPR Workshop on Document Analysis Systems (DAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128597368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Théodore Bluche, D. Stutzmann, Christopher Kermorvant
Written texts are both physical (signs, shapes and graphical systems) and abstract objects (ideas), whose meanings and social connotations evolve through time. To study this dual nature of texts, palaeographers need to analyse large scale corpora at the finest granularity, such as character shape. This goal can only be reached through an automatic segmentation process. In this paper, we present a method, based on Handwritten Text Recognition, to automatically align images of digitized manuscripts with texts from scholarly editions, at the levels of page, column, line, word, and character. It has been successfully applied to two datasets of medieval manuscripts, which are now almost fully segmented at character level. The quality of the word and character segmentations are evaluated and further palaeographical analysis are presented.
{"title":"Automatic Handwritten Character Segmentation for Paleographical Character Shape Analysis","authors":"Théodore Bluche, D. Stutzmann, Christopher Kermorvant","doi":"10.1109/DAS.2016.74","DOIUrl":"https://doi.org/10.1109/DAS.2016.74","url":null,"abstract":"Written texts are both physical (signs, shapes and graphical systems) and abstract objects (ideas), whose meanings and social connotations evolve through time. To study this dual nature of texts, palaeographers need to analyse large scale corpora at the finest granularity, such as character shape. This goal can only be reached through an automatic segmentation process. In this paper, we present a method, based on Handwritten Text Recognition, to automatically align images of digitized manuscripts with texts from scholarly editions, at the levels of page, column, line, word, and character. It has been successfully applied to two datasets of medieval manuscripts, which are now almost fully segmented at character level. The quality of the word and character segmentations are evaluated and further palaeographical analysis are presented.","PeriodicalId":197359,"journal":{"name":"2016 12th IAPR Workshop on Document Analysis Systems (DAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125870543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The task of text line extraction in images is a crucial prerequisite for content-based image understanding applications. In this paper, we propose a novel text line extraction method based on k-shortest paths global optimization in images. Firstly, the candidate connected components are extracted by reformulating it as Maximal Stable Extremal Region (MSER) results in images. Then, the directed graph is built upon the connected component nodes with edges comprising of unary and pairwise cost function. Finally, the text line extraction problem is solved using the k-shortest paths optimization algorithm by taking advantage of the particular structure of the directed graph. Experimental results on public dataset demonstrate the effectiveness of proposed method in comparison with state-of-the-art methods.
{"title":"Globally Optimal Text Line Extraction Based on K-Shortest Paths Algorithm","authors":"Liuan Wang, S. Uchida, Wei-liang Fan, Jun Sun","doi":"10.1109/DAS.2016.12","DOIUrl":"https://doi.org/10.1109/DAS.2016.12","url":null,"abstract":"The task of text line extraction in images is a crucial prerequisite for content-based image understanding applications. In this paper, we propose a novel text line extraction method based on k-shortest paths global optimization in images. Firstly, the candidate connected components are extracted by reformulating it as Maximal Stable Extremal Region (MSER) results in images. Then, the directed graph is built upon the connected component nodes with edges comprising of unary and pairwise cost function. Finally, the text line extraction problem is solved using the k-shortest paths optimization algorithm by taking advantage of the particular structure of the directed graph. Experimental results on public dataset demonstrate the effectiveness of proposed method in comparison with state-of-the-art methods.","PeriodicalId":197359,"journal":{"name":"2016 12th IAPR Workshop on Document Analysis Systems (DAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122944559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pen-based systems are becoming more and more important due to the growing availability of touch sensitive devices in various forms and sizes. Their interfaces offer the possibility to directly interact with a system by natural handwriting. In contrast to other input modalities it is not required to switch to special modes, like software-keyboards. In this paper we propose a new method for querying digital archives of historical documents. Word images are retrieved with respect to search terms that users write on a pen-based system by hand. The captured trajectory is used as a query which we call query-by-online-trajectory word spotting. By using attribute embeddings for both online-trajectory and visual features, word images are retrieved based on their distance to the query in a common subspace. The system is therefore robust, as no explicit transcription for queries or word images is required. We evaluate our approach for writer-dependent as well as writer-independent scenarios, where we present highly accurate retrieval results in the former and compelling retrieval results in the latter case. Our performance is very competitive in comparison to related methods from the literature.
{"title":"Word Spotting in Historical Document Collections with Online-Handwritten Queries","authors":"Christian Wieprecht, Leonard Rothacker, G. Fink","doi":"10.1109/DAS.2016.41","DOIUrl":"https://doi.org/10.1109/DAS.2016.41","url":null,"abstract":"Pen-based systems are becoming more and more important due to the growing availability of touch sensitive devices in various forms and sizes. Their interfaces offer the possibility to directly interact with a system by natural handwriting. In contrast to other input modalities it is not required to switch to special modes, like software-keyboards. In this paper we propose a new method for querying digital archives of historical documents. Word images are retrieved with respect to search terms that users write on a pen-based system by hand. The captured trajectory is used as a query which we call query-by-online-trajectory word spotting. By using attribute embeddings for both online-trajectory and visual features, word images are retrieved based on their distance to the query in a common subspace. The system is therefore robust, as no explicit transcription for queries or word images is required. We evaluate our approach for writer-dependent as well as writer-independent scenarios, where we present highly accurate retrieval results in the former and compelling retrieval results in the latter case. Our performance is very competitive in comparison to related methods from the literature.","PeriodicalId":197359,"journal":{"name":"2016 12th IAPR Workshop on Document Analysis Systems (DAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132820100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores the use of a learned classifier for post-OCR text correction. Experiments with the Arabic language show that this approach, which integrates a weighted confusion matrix and a shallow language model, improves the vast majority of segmentation and recognition errors, the most frequent types of error on our dataset.
{"title":"OCR Error Correction Using Character Correction and Feature-Based Word Classification","authors":"Ido Kissos, N. Dershowitz","doi":"10.1109/DAS.2016.44","DOIUrl":"https://doi.org/10.1109/DAS.2016.44","url":null,"abstract":"This paper explores the use of a learned classifier for post-OCR text correction. Experiments with the Arabic language show that this approach, which integrates a weighted confusion matrix and a shallow language model, improves the vast majority of segmentation and recognition errors, the most frequent types of error on our dataset.","PeriodicalId":197359,"journal":{"name":"2016 12th IAPR Workshop on Document Analysis Systems (DAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131166329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During past years, text extraction in document images has been widely studied in the general context of Document Image Analysis (DIA) and especially in the framework of layout analysis. Many existing techniques rely on complex processes based on preprocessing, image transforms or component/edges extraction and their analysis. At the same time, text extraction inside videos has received an increased interest and the use of corner or key points has been proven to be very effective. Because it is noteworthy to notice that very few studies were performed on the use of corner points for text extraction in document images, we propose in this paper to evaluate the possibilities associated with this kind of approach for DIA. To do that, we designed a very simple technique based on FAST key points. A first stage divide the image into blocks and the density of points inside each one is computed. The more dense ones are kept as text blocks. Then, connectivity of blocks is checked to group them and to obtain complete text blocks. This technique has been evaluated on different kind of images: different languages (Telugu, Arabic, French), handwritten as well as typewritten, skewed documents, images at different resolution and with different kind and amount of noises (deformations, ink dot, bleed through, acquisition (blur, resolution)), etc. Even with fixed parameters for all such kind of documents images, the precision and recall are close or higher to 90% which makes this basic method already effective. Consequently, even if the proposed approach does not propose a breakthrough from theoretical aspects, it highlights that accurate text extraction could be achieved without complex approach. Moreover, this approach could also be easily improved to be more precise, robust and useful for more complex layout analysis.
{"title":"Text Extraction in Document Images: Highlight on Using Corner Points","authors":"Vikas Yadav, N. Ragot","doi":"10.1109/DAS.2016.67","DOIUrl":"https://doi.org/10.1109/DAS.2016.67","url":null,"abstract":"During past years, text extraction in document images has been widely studied in the general context of Document Image Analysis (DIA) and especially in the framework of layout analysis. Many existing techniques rely on complex processes based on preprocessing, image transforms or component/edges extraction and their analysis. At the same time, text extraction inside videos has received an increased interest and the use of corner or key points has been proven to be very effective. Because it is noteworthy to notice that very few studies were performed on the use of corner points for text extraction in document images, we propose in this paper to evaluate the possibilities associated with this kind of approach for DIA. To do that, we designed a very simple technique based on FAST key points. A first stage divide the image into blocks and the density of points inside each one is computed. The more dense ones are kept as text blocks. Then, connectivity of blocks is checked to group them and to obtain complete text blocks. This technique has been evaluated on different kind of images: different languages (Telugu, Arabic, French), handwritten as well as typewritten, skewed documents, images at different resolution and with different kind and amount of noises (deformations, ink dot, bleed through, acquisition (blur, resolution)), etc. Even with fixed parameters for all such kind of documents images, the precision and recall are close or higher to 90% which makes this basic method already effective. Consequently, even if the proposed approach does not propose a breakthrough from theoretical aspects, it highlights that accurate text extraction could be achieved without complex approach. Moreover, this approach could also be easily improved to be more precise, robust and useful for more complex layout analysis.","PeriodicalId":197359,"journal":{"name":"2016 12th IAPR Workshop on Document Analysis Systems (DAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123341162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oussama Zayene, Mathias Seuret, Sameh Masmoudi Touj, J. Hennebert, R. Ingold, N. Amara
Text detection in videos is a challenging problem due to variety of text specificities, presence of complex background and anti-aliasing/compression artifacts. In this paper, we present an approach for horizontally aligned artificial text detection in Arabic news video. The novelty of this method revolves around the combination of two techniques: an adapted version of the Stroke Width Transform (SWT) algorithm and a convolutional auto-encoder (CAE). First, the SWT extracts text candidates' components. They are then filtered and grouped using geometric constraints and Stroke Width information. Second, the CAE is used as an unsupervised feature learning method to discriminate the obtained textline candidates as text or non-text. We assess the proposed approach on the public Arabic-Text-in-Video database (AcTiV-DB) using different evaluation protocols including data from several TV channels. Experiments indicate that the use of learned features significantly improves the text detection results.
{"title":"Text Detection in Arabic News Video Based on SWT Operator and Convolutional Auto-Encoders","authors":"Oussama Zayene, Mathias Seuret, Sameh Masmoudi Touj, J. Hennebert, R. Ingold, N. Amara","doi":"10.1109/DAS.2016.80","DOIUrl":"https://doi.org/10.1109/DAS.2016.80","url":null,"abstract":"Text detection in videos is a challenging problem due to variety of text specificities, presence of complex background and anti-aliasing/compression artifacts. In this paper, we present an approach for horizontally aligned artificial text detection in Arabic news video. The novelty of this method revolves around the combination of two techniques: an adapted version of the Stroke Width Transform (SWT) algorithm and a convolutional auto-encoder (CAE). First, the SWT extracts text candidates' components. They are then filtered and grouped using geometric constraints and Stroke Width information. Second, the CAE is used as an unsupervised feature learning method to discriminate the obtained textline candidates as text or non-text. We assess the proposed approach on the public Arabic-Text-in-Video database (AcTiV-DB) using different evaluation protocols including data from several TV channels. Experiments indicate that the use of learned features significantly improves the text detection results.","PeriodicalId":197359,"journal":{"name":"2016 12th IAPR Workshop on Document Analysis Systems (DAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121664488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
George Retsinas, G. Louloudis, N. Stamatopoulos, B. Gatos
In this paper, we present a novel approach for segmentation-based handwritten keyword spotting. The proposed approach relies upon the extraction of a simple yet efficient descriptor which is based on projections of oriented gradients. To this end, a global and a local word image descriptors, together with their combination, are proposed. Retrieval is performed using to the euclidean distance between the descriptors of a query image and the segmented word images. The proposed methods have been evaluated on the dataset of the ICFHR 2014 Competition on handwritten keyword spotting. Experimental results prove the efficiency of the proposed methods compared to several state-of-the-art techniques.
{"title":"Keyword Spotting in Handwritten Documents Using Projections of Oriented Gradients","authors":"George Retsinas, G. Louloudis, N. Stamatopoulos, B. Gatos","doi":"10.1109/DAS.2016.61","DOIUrl":"https://doi.org/10.1109/DAS.2016.61","url":null,"abstract":"In this paper, we present a novel approach for segmentation-based handwritten keyword spotting. The proposed approach relies upon the extraction of a simple yet efficient descriptor which is based on projections of oriented gradients. To this end, a global and a local word image descriptors, together with their combination, are proposed. Retrieval is performed using to the euclidean distance between the descriptors of a query image and the segmented word images. The proposed methods have been evaluated on the dataset of the ICFHR 2014 Competition on handwritten keyword spotting. Experimental results prove the efficiency of the proposed methods compared to several state-of-the-art techniques.","PeriodicalId":197359,"journal":{"name":"2016 12th IAPR Workshop on Document Analysis Systems (DAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124024583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Historical Chinese character recognition has been suffering from the problem of lacking sufficient labeled training samples. A transfer learning method based on Convolutional Neural Network (CNN) for historical Chinese character recognition is proposed in this paper. A CNN model L is trained by printed Chinese character samples in the source domain. The network structure and weights of model L are used to initialize another CNN model T, which is regarded as the feature extractor and classifier in the target domain. The model T is then fine-tuned by a few labeled historical or handwritten Chinese character samples, and used for final evaluation in the target domain. Several experiments regarding essential factors of the CNNbased transfer learning method are conducted, showing that the proposed method is effective.
{"title":"CNN Based Transfer Learning for Historical Chinese Character Recognition","authors":"Yejun Tang, Liangrui Peng, Qianxiong Xu, Yanwei Wang, Akio Furuhata","doi":"10.1109/DAS.2016.52","DOIUrl":"https://doi.org/10.1109/DAS.2016.52","url":null,"abstract":"Historical Chinese character recognition has been suffering from the problem of lacking sufficient labeled training samples. A transfer learning method based on Convolutional Neural Network (CNN) for historical Chinese character recognition is proposed in this paper. A CNN model L is trained by printed Chinese character samples in the source domain. The network structure and weights of model L are used to initialize another CNN model T, which is regarded as the feature extractor and classifier in the target domain. The model T is then fine-tuned by a few labeled historical or handwritten Chinese character samples, and used for final evaluation in the target domain. Several experiments regarding essential factors of the CNNbased transfer learning method are conducted, showing that the proposed method is effective.","PeriodicalId":197359,"journal":{"name":"2016 12th IAPR Workshop on Document Analysis Systems (DAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122178580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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