Knowledge Commons of Institute of Automation,CAS
Printed/Handwritten Texts and Graphics Separation in Complex Documents using Conditional Random Fields | |
Li, Xiao-Hui1,2; Yin, Fei1,2; Liu, Cheng-Lin1,2,3 | |
2018 | |
会议名称 | The 13th IAPR International Workshop on Document Analysis Systems |
会议日期 | 2018-4 |
会议地点 | 奥地利维也纳维也纳工业大学 |
出版者 | IEEE |
摘要 | In this paper we propose a structured prediction based system for text/non-text classification and printed/handwritten texts separation at connected component (CC) level in complex documents. We formulate the separation of different elements as joint classification problems and use conditional random fields (CRFs) to integrate both local and contextual information for improving the classification accuracy. Both our unary and pairwise potentials are formulated as neural networks for better exploiting contextual information. Considering the different properties in text/non-text classification and printed/handwritten texts separation, we use multilayer perception (MLP) and convolutional neural network (CNN) for potentials, respectively. To evaluate the performance of the proposed method, we provide a test paper document database named TestPaper1.0, which can be used for many other tasks as well. Our method achieve impressive results for both tasks on TestPaper1.0 dataset. Moreover, even with very shallow CNNs as potentials, our method achieves state-of-the-art performance for writing type (printed/handwritten) separation on the highly heterogeneous Maurdor dataset, surpassing Maurdor2013 and Maurdor2014 campaign winners. This demonstrates the effectiveness and superiority of our method. |
关键词 | text/non-text document understanding structured prediction printed/handwritten |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China (NSFC)[61733007] ; National Natural Science Foundation of China (NSFC)[61411136002] ; National Natural Science Foundation of China (NSFC)[61721004] ; National Natural Science Foundation of China (NSFC)[61721004] ; National Natural Science Foundation of China (NSFC)[61411136002] ; National Natural Science Foundation of China (NSFC)[61733007] |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44414 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
通讯作者 | Liu, Cheng-Lin |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences 95 Zhongguancun East Road, Beijing 100190, P.R. China 2.University of Chinese Academy of Sciences, Beijing, P.R. China 3.CAS Center for Excellence of Brain Science and Intelligence Technology, Beijing, P.R. China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Li, Xiao-Hui,Yin, Fei,Liu, Cheng-Lin. Printed/Handwritten Texts and Graphics Separation in Complex Documents using Conditional Random Fields[C]:IEEE,2018. |
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