2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)最新文献_第5页

Automating Transliteration of Cuneiform from Parallel Lines with Sparse Data 稀疏数据下平行线中楔形文字的自动音译

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.106

B. Bogacz, Maximilian Klingmann, H. Mara

{"title":"Automating Transliteration of Cuneiform from Parallel Lines with Sparse Data","authors":"B. Bogacz, Maximilian Klingmann, H. Mara","doi":"10.1109/ICDAR.2017.106","DOIUrl":"https://doi.org/10.1109/ICDAR.2017.106","url":null,"abstract":"Cuneiform tablets appertain to the oldest textual artifacts and are in extent comparable to texts written in Latin or ancient Greek. The Cuneiform Commentaries Project (CPP) from Yale University provides tracings of cuneiform tablets with annotated transliterations and translations. As a part of our work analyzing cuneiform script computationally with 3D-acquisition and word-spotting, we present a first approach for automatized learning of transliterations of cuneiform tablets based on a corpus of parallel lines. These consist of manually drawn cuneiform characters and their transliteration into an alphanumeric code. Since the Cuneiform script is only available as raster-data, we segment lines with a projection profile, extract Histogram of oriented Gradients (HoG) features, detect outliers caused by tablet damage, and align those features with the transliteration. We apply methods from part-of-speech tagging to learn a correspondence between features and transliteration tokens. We evaluate point-wise classification with K-Nearest Neighbors (KNN) and a Support Vector Machine (SVM); sequence classification with a Hidden Markov Model (HMM) and a Structured Support Vector Machine (SVM-HMM). Analyzing our findings, we reach the conclusion that the sparsity of data, inconsistent labeling and the variety of tracing styles do currently not allow for fully automatized transliterations with the presented approach. However, the pursuit of automated learning of transliterations is of great relevance as manual annotation in larger quantities is not viable, given the few experts capable of transcribing cuneiform tablets.","PeriodicalId":433676,"journal":{"name":"2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131340011","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: 5

PhyloParser: A Hybrid Algorithm for Extracting Phylogenies from Dendrograms PhyloParser:一种从树形图中提取系统发生的混合算法

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.180

Po-Shen Lee, Sean T. Yang, Jevin D. West, Bill Howe

{"title":"PhyloParser: A Hybrid Algorithm for Extracting Phylogenies from Dendrograms","authors":"Po-Shen Lee, Sean T. Yang, Jevin D. West, Bill Howe","doi":"10.1109/ICDAR.2017.180","DOIUrl":"https://doi.org/10.1109/ICDAR.2017.180","url":null,"abstract":"We consider a new approach to extracting information from dendrograms in the biological literature representing phylogenetic trees. Existing algorithmic approaches to extract these relationships rely on tracing tree contours and are very sensitive to image quality issues, but manual approaches require significant human effort and cannot be used at scale. We introduce PhyloParser, a fully automated, end-to-end system for automatically extracting species relationships from phylogenetic tree diagrams using a multi-modal approach to digest diverse tree styles. Our approach automatically identifies phylogenetic tree figures in the scientific literature, extracts the key components of tree structure, reconstructs the tree, and recovers the species relationships. We use multiple methods to extract tree components with high recall, then filter false positives by applying topological heuristics about how these components fit together. We present an evaluation on a real-world dataset to quantitatively and qualitatively demonstrate the efficacy of our approach. Our classifier achieves 89% recall and 99% precision, with a low average error rate relative to previous approaches. We aim to use PhyloParser to build a linked, open, comprehensive database of phylogenetic information that covers the historical literature as well as current data, and then use this resource to identify areas of disagreement and poor coverage in the biological literature.","PeriodicalId":433676,"journal":{"name":"2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131916609","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: 7

ICDAR2017 Robust Reading Challenge on COCO-Text ICDAR2017基于COCO-Text的稳健阅读挑战

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.234

Raul Gomez, Baoguang Shi, L. G. I. Bigorda, Lukás Neumann, Andreas Veit, Jiri Matas, Serge J. Belongie, Dimosthenis Karatzas

引用次数: 45

Classification and Information Extraction for Complex and Nested Tabular Structures in Images 图像中复杂和嵌套表格结构的分类与信息提取

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.191

A. Riad, Christopher Sporer, S. S. Bukhari, A. Dengel

引用次数: 8

Error Detection and Corrections in Indic OCR Using LSTMs 基于lstm的索引OCR错误检测与校正

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.13

Rohit Saluja, D. Adiga, P. Chaudhuri, Ganesh Ramakrishnan, Mark James Carman

{"title":"Error Detection and Corrections in Indic OCR Using LSTMs","authors":"Rohit Saluja, D. Adiga, P. Chaudhuri, Ganesh Ramakrishnan, Mark James Carman","doi":"10.1109/ICDAR.2017.13","DOIUrl":"https://doi.org/10.1109/ICDAR.2017.13","url":null,"abstract":"Conventional approaches to spell checking suggest spelling corrections using proximity-based matches to a known vocabulary. For highly inflectional Indian languages, any off-the-shelf vocabulary is significantly incomplete, since a large fraction of words in Indic documents are generated using word conjoining rules. Therefore, a tremendous manual effort is needed in spell-correcting words in Indic OCR documents. Moreover, in a spell checking system, a vocabulary may suggest multiple alternatives to the incorrect word. The ranking of these corrective suggestions is improved using language models. Owing to corpus resource scarcity, however, Indian languages lack reliable language models. Thus, learning the character (or n-gram) confusions or error patterns of the OCR system can be helpful in correcting the Out of Vocabulary (OOV) words in OCR documents. We adopt a Long Short-Term Memory (LSTM) based character level language model with a fixed delay for discriminative language modeling in the context of OCR errors for jointly addressing the problems of error detection and correction in Indic OCR. For words that need not be corrected in the OCR output, our model simply abstains from suggesting any changes. We present extensive results to validate the performance of our model on four Indian languages with different inflectional complexities. We achieve F-Scores above 92.4% and decreases in Word Error Rates (WER) of at least 26.7% across the four languages.","PeriodicalId":433676,"journal":{"name":"2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132102150","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: 20

Local Binary Patterns for Document Forgery Detection 用于文档伪造检测的局部二进制模式

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.202

Francisco Cruz, Nicolas Sidère, Mickaël Coustaty, V. P. d'Andecy, J. Ogier

引用次数: 25

A Unified Video Text Detection Method with Network Flow 基于网络流的统一视频文本检测方法

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.62

Xue-Hang Yang, Wenhao He, Fei Yin, Cheng-Lin Liu

{"title":"A Unified Video Text Detection Method with Network Flow","authors":"Xue-Hang Yang, Wenhao He, Fei Yin, Cheng-Lin Liu","doi":"10.1109/ICDAR.2017.62","DOIUrl":"https://doi.org/10.1109/ICDAR.2017.62","url":null,"abstract":"Scene text detection in videos has many application needs but has drawn less attention than that in images. Existing methods for video text detection perform unsatisfactorily because of the insufficient utilization of spatial and temporal information. In this paper, we propose a novel video text detection method with network flow based tracking. The system first applies a newly proposed Fully Convolutional Neural Network (FCN) based scene text detection method to detect texts in individual frames and then track proposals in adjacent frames with a motion-based method. Next, the text association problem is formulated into a cost-flow network and text trajectories are derived from the network with a min-cost flow algorithm. At last, the trajectories are post-processed to improve the precision accuracy. The method can detect multi-oriented scene text in videos and incorporate spatial and temporal information efficiently. Experimental results show that the method improves the detection performance remarkably on benchmark datasets, e.g., by a 15.66% increase of ATA Average Tracking Accuracy) on ICDAR video scene text dataset.","PeriodicalId":433676,"journal":{"name":"2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115904404","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: 8

ICDAR2017 Competition on Information Extraction in Historical Handwritten Records ICDAR2017历史手写记录信息提取竞赛

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.227

A. Fornés, Verónica Romero, Arnau Baró, J. I. Toledo, Joan Andreu Sánchez, E. Vidal, J. Lladós

引用次数: 26

Integrating Bilingual Named Entities Lexicon with Conditional Random Fields Model for Arabic Named Entities Recognition 集成双语命名实体词典和条件随机场模型的阿拉伯语命名实体识别

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.105

Emna Hkiri, Souheyl Mallat, M. Zrigui

引用次数: 5

Automatic Elevation Datum Detection and Hyperlinking of Architecture, Engineering & Construction Documents 建筑、工程和施工文件的自动高程基准检测和超链接

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.266

P. Banerjee, Supriya Das, B. Seraogi, Himadri Majumdar, Srinivas Mukkamala, Rahul Roy, B. Chaudhuri

{"title":"Automatic Elevation Datum Detection and Hyperlinking of Architecture, Engineering & Construction Documents","authors":"P. Banerjee, Supriya Das, B. Seraogi, Himadri Majumdar, Srinivas Mukkamala, Rahul Roy, B. Chaudhuri","doi":"10.1109/ICDAR.2017.266","DOIUrl":"https://doi.org/10.1109/ICDAR.2017.266","url":null,"abstract":"In AEC (Architecture, Engineering & Construction) industry drawing documents are used as a blueprint to facilitate the construction process. It is also a graphical language that communicates ideas and information from one mind to another. A construction project normally contains huge number of such drawing documents. An engineer or architect often needs to refer different documents while drawing a new one or marking some irregularity or real construction. Elevation datum is one of the graphical representation for referring one document to another. It will be a very difficult and time-consuming task manually to identify elevation datum and link a file with respect to each datum. Our suggested method is aimed to overcome this hurdle. Therefore, the proposed system will automatically find the elevation datums from the existing drawing documents and will also create hyperlinks to enable the engineer to quickly navigate among the drawing files. We have achieved overall accuracy of 95.28% for elevation datum detection and accurate destination document text recognition.","PeriodicalId":433676,"journal":{"name":"2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","volume":"432 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123604582","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: 1