2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)最新文献

The MUMTDB Dataset for Evaluating Simultaneous Composition of Structured Documents in a Multi-user and Multi-touch Environment 在多用户和多点触摸环境中评估结构化文档同时组合的MUMTDB数据集

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-23 DOI: 10.1109/ICFHR.2016.0077

Zhaoxin Chen, Éric Anquetil, H. Mouchère, C. Viard-Gaudin

引用次数: 1

An Efficient New PDE-based Characters Reconstruction after Graphics Removal 一种高效的基于pde的图形移除后字符重建方法

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-23 DOI: 10.1109/ICFHR.2016.0088

Louisa Kessi, Frank Lebourgeois, Christophe Garcia

{"title":"An Efficient New PDE-based Characters Reconstruction after Graphics Removal","authors":"Louisa Kessi, Frank Lebourgeois, Christophe Garcia","doi":"10.1109/ICFHR.2016.0088","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0088","url":null,"abstract":"The separation between texts and graphics when they are overlapped is a challenging problem for digitization companies. In a previous work [1], we presented the first unsupervised fully automatic segmentation system adapted for colour business document with significant colour complexity and dithered background. The system achieves several operations to segment automatically colour images, separate text from noise and graphics and provides colour information about text colour. After split overlapped characters and separates characters from graphics, characters are broken. The OCR system becomes unable to recognize successfully broken characters and its efficiency is thus seriously affected. This paper presents the first Character Reconstruction System through a new PDE (Partial Differential Equation)-based approach. Our approach takes benefit of the combination of the anisotropic morphology proposed by Breuß and the Weickert Coherence enhancing shock filter diffusion. We introduce and present a continuous anisotropic morphology method driven by the main direction of the first order tensors applied in the neighborhood of the missing part left by the separation between text and graphics. It reconstructs the missing part even when the left area is larger than the strokes width. The coherency of the orientation of the tensors around missing parts overcomes the problem of image noises. The application of the ABBY FineReader OCR engine proves an important reduction in OCR errors. Our experiments show that our proposition compared to the existing state of the art requires no training steps and outperforms both of anisotropic morphology and the Weickert Coherence enhancing shock filter diffusion applied separately.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115466518","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: 2

SpottingNet: Learning the Similarity of Word Images with Convolutional Neural Network for Word Spotting in Handwritten Historical Documents SpottingNet:用卷积神经网络学习单词图像的相似度，用于手写体历史文献中的单词识别

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-23 DOI: 10.1109/ICFHR.2016.0063

Zhuoyao Zhong, Weishen Pan, Lianwen Jin, H. Mouchère, C. Viard-Gaudin

{"title":"SpottingNet: Learning the Similarity of Word Images with Convolutional Neural Network for Word Spotting in Handwritten Historical Documents","authors":"Zhuoyao Zhong, Weishen Pan, Lianwen Jin, H. Mouchère, C. Viard-Gaudin","doi":"10.1109/ICFHR.2016.0063","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0063","url":null,"abstract":"Word spotting is a content-based retrieval process that obtains a ranked list of word image candidates similar to the query word in digital document images. In this paper, we present a convolutional neural network (CNN) based end-to-end approach for Query-by-Example (QBE) word spotting in handwritten historical documents. The presented models enable conjointly learning the representative word image descriptors and evaluating the similarity measure between word descriptors directly from the word image, which are the two crucial factors in this task. We propose a similarity score fusion method integrated with hybrid deep-learning classifica-tion and regression models to enhance word spotting perfor-mance. In addition, we present a sample generation method using location jitter to balance similar and dissimilar image pairs and enlarge the dataset. Experiments are conducted on the George Washington (GW) dataset without involving any recognition methods or prior word category information. Our experiments show that the proposed model yields a new state-of-the-art mean average precision (mAP) of 80.03%, significantly outperforming previous results.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115143481","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: 17

A New Scheme for Text Line and Character Segmentation from Gray Scale Images of Palm Leaf Manuscript 棕榈叶手稿灰度图像文本线与字符分割新方案

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-23 DOI: 10.1109/ICFHR.2016.0068

M. W. A. Kesiman, J. Burie, J. Ogier

引用次数: 19

ICFHR2016 Competition on the Analysis of Handwritten Text in Images of Balinese Palm Leaf Manuscripts ICFHR2016峇里棕榈叶手稿图像手写体文字分析竞赛

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-23 DOI: 10.1109/ICFHR.2016.0114

J. Burie, Mickaël Coustaty, S. Hadi, M. W. A. Kesiman, J. Ogier, E. Paulus, Kimheng Sok, I. M. G. Sunarya, Dona Valy

{"title":"ICFHR2016 Competition on the Analysis of Handwritten Text in Images of Balinese Palm Leaf Manuscripts","authors":"J. Burie, Mickaël Coustaty, S. Hadi, M. W. A. Kesiman, J. Ogier, E. Paulus, Kimheng Sok, I. M. G. Sunarya, Dona Valy","doi":"10.1109/ICFHR.2016.0114","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0114","url":null,"abstract":"This paper presents the results of the Competition on the Analysis of Handwritten Text in Images of Balinese Palm Leaf Manuscripts that was organized in the context of the 15th International Conference on Frontiers in Handwriting Recognition (ICFHR-2016). This competition provides a suitable challenge for testing and evaluation of robustness for some methods, image features and descriptors which were already proposed for handwritten text analysis of document image. In this competition, three different challenges in document analysis of palm leaf manuscript images are proposed: Challenge 1: Binarization of Palm Leaf Manuscript Images, Challenge 2: Query-by-Example Word Spotting on Palm Leaf Manuscript Images, and Challenge 3: Isolated Character Recognition of Balinese Script in Palm Leaf Manuscript Images. The first handwritten Balinese palm leaf manuscript dataset, the AMADI_LontarSet, is used for performance evaluation. This paper describes the competition details including the dataset creation and the ground truth construction, the evaluation measures used, a short description of each participant as well as the performance of the all submitted methods.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122860558","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: 29

ICFHR2016 CROHME: Competition on Recognition of Online Handwritten Mathematical Expressions ICFHR2016 CROHME:在线手写数学表达式识别竞赛

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-23 DOI: 10.1109/ICFHR.2016.0116

H. Mouchère, C. Viard-Gaudin, R. Zanibbi, Utpal Garain

{"title":"ICFHR2016 CROHME: Competition on Recognition of Online Handwritten Mathematical Expressions","authors":"H. Mouchère, C. Viard-Gaudin, R. Zanibbi, Utpal Garain","doi":"10.1109/ICFHR.2016.0116","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0116","url":null,"abstract":"This paper presents an overview of the 5th Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME). As in previous years, the main task is formula recognition from handwritten strokes (Task 1). Additional tasks include classification of isolated symbols (Task 2a), classification of isolated valid and invalid symbols (Task 2b), a new task on parsing formula structure from valid handwritten symbols (Task 3), and parsing expressions with matrices (Task 4, experimental). In total, eleven (11) research labs registered for the competition, with six (6) teams submitting results. Innovations for this CROHME included providing a corpus of formulae from Wikipedia to train language models, and an online system for result submission. The highest recognition rates were obtained by MyScript corporation (Task 1. 67.65%, 2a. 92.81%, 2b. 86.77%, 3. 84.38%, and 4. 68.40%). Using only provided training data, the highest recognition rates were obtained by WIRIS corporation (Task 1. 49.61%, Task 3. 78.80%, Task 4. 56.40%), the Tokyo University of Agriculture and Technology (Task 2a. 92.28%), and RIT (Task 2b. 83.34%). The competition results suggest that recognition of handwritten formulae remains a difficult structural pattern recognition task.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133730599","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: 79

An Investigation of Novel Combined Features for a Handwritten Short Answer Assessment System 手写简答评价系统的新型组合特征研究

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-23 DOI: 10.1109/ICFHR.2016.0031

Hemmaphan Suwanwiwat, U. Pal, M. Blumenstein

引用次数: 0

Online Handwritten Mongolian Word Recognition Using MWRCNN and Position Maps 基于MWRCNN和位置图的在线手写蒙古语词识别

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0024

Ji Liu, Long-Long Ma, Jian Wu

引用次数: 6

New Tampered Features for Scene and Caption Text Classification in Video Frame 视频帧中场景和字幕文本分类的新篡改特征

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0020

Sangheeta Roy, P. Shivakumara, U. Pal, Tong Lu, C. Tan

{"title":"New Tampered Features for Scene and Caption Text Classification in Video Frame","authors":"Sangheeta Roy, P. Shivakumara, U. Pal, Tong Lu, C. Tan","doi":"10.1109/ICFHR.2016.0020","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0020","url":null,"abstract":"The presence of both caption/graphics/superimposed and scene texts in video frames is the major cause for the poor accuracy of text recognition methods. This paper proposes an approach for identifying tampered information by analyzing the spatial distribution of DCT coefficients in a new way for classifying caption and scene text. Since caption text is edited/superimposed, which results in artificially created texts comparing to scene texts that exist naturally in frames. We exploit this fact to identify the presence of caption and scene texts in video frames based on the advantage of DCT coefficients. The proposed method analyzes the distributions of both zero and non-zero coefficients (only positive values) locally by moving a window, and studies histogram operations over each input text line image. This generates line graphs for respective zero and non-zero coefficient coordinates. We further study the behavior of text lines, namely, linearity and smoothness based on centroid location analysis, and the principal axis direction of each text line for classification. Experimental results on standard datasets, namely, ICDAR 2013 video, 2015 video, YVT video and our own data, show that the performances of text recognition methods are improved significantly after-classification compared to before-classification.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125944600","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: 12

A Candidate Lattice Refinement Method for Online Handwritten Japanese Text Recognition 在线手写体日语文本识别的候选点阵细化方法

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0050

Jianjuan Liang, Bilan Zhu, M. Nakagawa

{"title":"A Candidate Lattice Refinement Method for Online Handwritten Japanese Text Recognition","authors":"Jianjuan Liang, Bilan Zhu, M. Nakagawa","doi":"10.1109/ICFHR.2016.0050","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0050","url":null,"abstract":"This paper presents a candidate lattice refinement method for online handwritten Japanese text recognition. In the integrated segmentation-recognition framework, we first over-segment a character string pattern into primitive segments at least at their true boundaries so that each primitive segment may compose a single character or a part of a character. Then a candidate lattice is constructed based on the primitive segments. We search within the candidate lattice to obtain the optimal path as recognition result. In striving for high recognition accuracy, however, the approach must generate many candidate lattice nodes, which ultimately increase the recognition time. To solve this problem, we refine the candidate lattice to eliminate unnecessary nodes before path search and text recognition. For the refinement, we evaluate all segmentation hypotheses by combining the probability of a character verifier using noncharacter samples, the class-independent unary and binary geometric context, as well as character segmentation. We retain N-best paths by beam search to reduce the complexity of the candidate lattice. Experiments on horizontal text lines extracted from the Kondate database show that the proposed method keeps recognition accuracy while reducing recognition time to half.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123270465","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: 2