2019 International Conference on Document Analysis and Recognition (ICDAR)最新文献_第4页

Hybrid Training Data for Historical Text OCR 历史文本OCR混合训练数据

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00096

J. Martínek, Ladislav Lenc, P. Král, Anguelos Nicolaou, V. Christlein

{"title":"Hybrid Training Data for Historical Text OCR","authors":"J. Martínek, Ladislav Lenc, P. Král, Anguelos Nicolaou, V. Christlein","doi":"10.1109/ICDAR.2019.00096","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00096","url":null,"abstract":"Current optical character recognition (OCR) systems commonly make use of recurrent neural networks (RNN) that process whole text lines. Such systems avoid the task of character segmentation necessary for character-based approaches. A disadvantage of this approach is a need of a large amount of annotated data. This can be solved by sing generated synthetic data instead of costly manually annotated ones. Unfortunately, such data is often not suitable for historical documents particularly for quality reasons. This work presents a hybrid approach for generating annotated data for OCR at a low cost. We first collect a small dataset of isolated characters from historical document images. Then, we generate historical looking text lines from the generated characters. Another contribution lies in the design and implementation of an OCR system based on a convolutional-LSTM network. We first pre-train this system on hybrid data. Afterwards, the network is fine-tuned with real printed text lines. We demonstrate that this training strategy is efficient for obtaining state-of-the-art results. We also show that the score of the proposed system is comparable or even better in comparison to several state-of-the-art systems.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122076164","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

Cascaded Detail-Preserving Networks for Super-Resolution of Document Images 用于文档图像超分辨率的级联细节保留网络

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00047

Zhichao Fu, Yu Kong, Yingbin Zheng, Hao Ye, Wenxin Hu, Jing Yang, Liang He

{"title":"Cascaded Detail-Preserving Networks for Super-Resolution of Document Images","authors":"Zhichao Fu, Yu Kong, Yingbin Zheng, Hao Ye, Wenxin Hu, Jing Yang, Liang He","doi":"10.1109/ICDAR.2019.00047","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00047","url":null,"abstract":"The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the low-resolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image super-resolution. Our model is composed by the Detail-Preserving Networks with small magnification. The loss function with perceptual terms is designed to simultaneously preserve the original patterns and enhance the edge of the characters. These networks are trained with the same architecture and different parameters and then assembled into a pipeline model with a larger magnification. The low-resolution images can upscale gradually by passing through each Detail-Preserving Network until the final high-resolution images. Through extensive experiments on two scanning document image datasets, we demonstrate that the proposed approach outperforms recent state-of-the-art image super-resolution methods, and combining it with standard OCR system lead to signification improvements on the recognition results.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116834359","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: 6

Detecting Named Entities in Unstructured Bengali Manuscript Images 检测非结构化孟加拉语手稿图像中的命名实体

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00040

Chandranath Adak, B. Chaudhuri, Chin-Teng Lin, M. Blumenstein

引用次数: 0

Binarization of Degraded Document Images using Convolutional Neural Networks Based on Predicted Two-Channel Images 基于预测双通道图像的卷积神经网络退化文档图像二值化

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00160

Y. Akbari, A. Britto, S. Al-Maadeed, Luiz Oliveira

引用次数: 18

ReS2TIM: Reconstruct Syntactic Structures from Table Images ReS2TIM:从表图像中重建语法结构

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00125

Wenyuan Xue, Qingyong Li, D. Tao

引用次数: 27

A New Document Image Quality Assessment Method Based on Hast Derivations 一种基于哈斯特导数的文档图像质量评估新方法

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00201

Alireza Alaei

{"title":"A New Document Image Quality Assessment Method Based on Hast Derivations","authors":"Alireza Alaei","doi":"10.1109/ICDAR.2019.00201","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00201","url":null,"abstract":"With the rapid emergence of new technologies, a voluminous number of images including document images is generated every day. Considering the volume of data and complexity of processes, manual analysis, annotation, recognition, classification, and retrieval, of such document images is impossible. To automatically deal with such processes, many document image analysis applications exist in the literature and many of them are currently in place in different organisation and institutes. The performance of those applications are directly affected by the quality of document images. Therefore, a document image quality assessment (DIQA) method is of primary need to allow users capture, compress and forward good quality (readable) document images to various information systems, such as online business and insurance, for further processing. To assess the quality of document images, this paper proposes a new full-reference DIQA method using first followed by second order Hast derivations. A similarity map is then created using second order Hast derivation maps obtained by employing Hast filters on both reference and distorted images. An average pooling is then employed to obtain a quality score for the distorted document image. To evaluate the proposed method, two different datasets were used. Both datasets are composed of images with the mean human opinion scores (MHOS) considered as ground truth. The results obtained from the proposed DIQA method are superior to the results reported in the literature.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125687967","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: 6

Layout Analysis on Challenging Historical Arabic Manuscripts using Siamese Network 利用Siamese网络分析阿拉伯历史手稿的布局

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00123

Reem Alaasam, Berat Kurar, Jihad El-Sana

引用次数: 10

Cross-Modal Prototype Learning for Zero-Shot Handwriting Recognition 零射击手写识别的跨模态原型学习

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00100

Xiang Ao, Xu-Yao Zhang, Hong-Ming Yang, Fei Yin, Cheng-Lin Liu

{"title":"Cross-Modal Prototype Learning for Zero-Shot Handwriting Recognition","authors":"Xiang Ao, Xu-Yao Zhang, Hong-Ming Yang, Fei Yin, Cheng-Lin Liu","doi":"10.1109/ICDAR.2019.00100","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00100","url":null,"abstract":"In contrast to machine recognizers that rely on training with large handwriting data, humans can recognize handwriting accurately on learning from few samples, and can even generalize to handwritten characters from printed samples. Simulating this ability in machine recognition is important to alleviate the burden of labeling large handwriting data, especially for large category set as in Chinese text. In this paper, inspired by human learning, we propose a cross-modal prototype learning (CMPL) method for zero-shot online handwritten character recognition: for unseen categories, handwritten characters can be recognized without learning from handwritten samples, but instead from printed characters. Particularly, the printed characters (one for each class) are embedded into a convolutional neural network (CNN) feature space to obtain prototypes representing each class, while the online handwriting trajectories are embedded with a recurrent neural network (RNN). Via cross-modal joint learning, handwritten characters can be recognized according to the printed prototypes. For unseen categories, handwritten characters can be recognized by only feeding a printed sample per category. Experiments on a benchmark Chinese handwriting database have shown the effectiveness and potential of the proposed method for zero-shot handwriting recognition.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123414889","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

An End-to-End Trainable Framework for Joint Optimization of Document Enhancement and Recognition 文档增强和识别联合优化的端到端可训练框架

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00019

Anupama Ray, Manoj Sharma, Avinash Upadhyay, Megh Makwana, S. Chaudhury, Akkshita Trivedi, Ajay Pratap Singh, Anil K. Saini

{"title":"An End-to-End Trainable Framework for Joint Optimization of Document Enhancement and Recognition","authors":"Anupama Ray, Manoj Sharma, Avinash Upadhyay, Megh Makwana, S. Chaudhury, Akkshita Trivedi, Ajay Pratap Singh, Anil K. Saini","doi":"10.1109/ICDAR.2019.00019","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00019","url":null,"abstract":"Recognizing text from degraded and low-resolution document images is still an open challenge in the vision community. Existing text recognition systems require a certain resolution and fails if the document is of low-resolution or heavily degraded or noisy. This paper presents an end-to-end trainable deep-learning based framework for joint optimization of document enhancement and recognition. We are using a generative adversarial network (GAN) based framework to perform image denoising followed by deep back projection network (DBPN) for super-resolution and use these super-resolved features to train a bidirectional long short term memory (BLSTM) with Connectionist Temporal Classification (CTC) for recognition of textual sequences. The entire network is end-to-end trainable and we obtain improved results than state-of-the-art for both the image enhancement and document recognition tasks. We demonstrate results on both printed and handwritten degraded document datasets to show the generalization capability of our proposed robust framework.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126097506","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: 10

Document Binarization via Multi-resolutional Attention Model with DRD Loss 基于DRD损失的多分辨率注意力模型的文档二值化

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00017

Xujun Peng, Chao Wang, Huaigu Cao

引用次数: 17