2016 12th IAPR Workshop on Document Analysis Systems (DAS)最新文献_第6页

Document Image Quality Assessment Using Discriminative Sparse Representation 基于判别稀疏表示的文档图像质量评估

2016 12th IAPR Workshop on Document Analysis Systems (DAS) Pub Date : 2016-04-11 DOI: 10.1109/DAS.2016.24

Xujun Peng, Huaigu Cao, P. Natarajan

{"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":"13 1","pages":"0"},"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}

引用次数: 14

Increasing Robustness of Handwriting Recognition Using Character N-Gram Decoding on Large Lexica 基于字符N-Gram解码的手写识别鲁棒性研究

2016 12th IAPR Workshop on Document Analysis Systems (DAS) Pub Date : 2016-04-11 DOI: 10.1109/DAS.2016.43

M. Schall, M. Schambach, M. Franz

{"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":"92 1","pages":"0"},"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}

引用次数: 3

Automatic Handwritten Character Segmentation for Paleographical Character Shape Analysis 用于古文字形状分析的自动手写字符分割

2016 12th IAPR Workshop on Document Analysis Systems (DAS) Pub Date : 2016-04-11 DOI: 10.1109/DAS.2016.74

Théodore Bluche, D. Stutzmann, Christopher Kermorvant

引用次数: 1

Globally Optimal Text Line Extraction Based on K-Shortest Paths Algorithm 基于k -最短路径算法的全局最优文本行提取

2016 12th IAPR Workshop on Document Analysis Systems (DAS) Pub Date : 2016-04-11 DOI: 10.1109/DAS.2016.12

Liuan Wang, S. Uchida, Wei-liang Fan, Jun Sun

引用次数: 4

Word Spotting in Historical Document Collections with Online-Handwritten Queries 联机手写查询在历史文献馆藏中的词识别

2016 12th IAPR Workshop on Document Analysis Systems (DAS) Pub Date : 2016-04-11 DOI: 10.1109/DAS.2016.41

Christian Wieprecht, Leonard Rothacker, G. Fink

{"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":"43 1","pages":"0"},"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}

引用次数: 3

OCR Error Correction Using Character Correction and Feature-Based Word Classification 基于字符校正和特征词分类的OCR纠错

2016 12th IAPR Workshop on Document Analysis Systems (DAS) Pub Date : 2016-04-11 DOI: 10.1109/DAS.2016.44

Ido Kissos, N. Dershowitz

引用次数: 67

Text Extraction in Document Images: Highlight on Using Corner Points 文档图像中的文本提取:使用角点突出显示

2016 12th IAPR Workshop on Document Analysis Systems (DAS) Pub Date : 2016-04-11 DOI: 10.1109/DAS.2016.67

Vikas Yadav, N. Ragot

{"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":"2 1","pages":"0"},"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}

引用次数: 18

Text Detection in Arabic News Video Based on SWT Operator and Convolutional Auto-Encoders 基于SWT算子和卷积自编码器的阿拉伯语新闻视频文本检测

2016 12th IAPR Workshop on Document Analysis Systems (DAS) Pub Date : 2016-04-11 DOI: 10.1109/DAS.2016.80

Oussama Zayene, Mathias Seuret, Sameh Masmoudi Touj, J. Hennebert, R. Ingold, N. Amara

引用次数: 24

Keyword Spotting in Handwritten Documents Using Projections of Oriented Gradients 利用定向梯度投影在手写文档中识别关键字

2016 12th IAPR Workshop on Document Analysis Systems (DAS) Pub Date : 2016-04-11 DOI: 10.1109/DAS.2016.61

George Retsinas, G. Louloudis, N. Stamatopoulos, B. Gatos

引用次数: 20

CNN Based Transfer Learning for Historical Chinese Character Recognition 基于CNN的古汉字识别迁移学习

2016 12th IAPR Workshop on Document Analysis Systems (DAS) Pub Date : 2016-04-11 DOI: 10.1109/DAS.2016.52

Yejun Tang, Liangrui Peng, Qianxiong Xu, Yanwei Wang, Akio Furuhata

引用次数: 42