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首页> 外文会议>Asilomar Conference on Signals, Systems and Computers >On noise reduction for handwritten writer identification
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On noise reduction for handwritten writer identification

机译：关于减少手写作者识别的噪声

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摘要

Academic work in identifying writers of handwritten documents has previously focused on clean benchmark datasets: plain white documents with uniform writing instruments. Solutions on this type of data have achieved hit-in-top-10 accuracy rates reaching upwards of 98%. Unfortunately, transferring competitive techniques to handwritten documents with noise is nontrivial, where performance drops by two-thirds. Noise in the context of handwritten documents can manifest itself in many ways, from irrelevant structured additions, e.g., graph paper, to unstructured partial occlusion, e.g. coffee stains and stamps. Additional issues that confound algorithmic writer identification solutions include the use of different writing implement, age, and writing state of mind. The proposed work explores training denoising neural networks to aid in identifying authors of handwritten documents. Our algorithms are trained on existing clean datasets artificially augmented with noise, and we evaluate them on a commissioned dataset, which features a diverse but balanced set of writers, writing implements, and writing substrates (incorporating various types of noise). Using the proposed denoising algorithm, we exceed the state of the art in writer identification of noisy handwritten documents by a significant margin.
机译：识别手写文档作者的学术工作以前集中在干净的基准数据集上：带有统一书写工具的纯白色文档。这类数据的解决方案已经达到了前10名的准确率，达到98％以上。不幸的是，将竞争性技术转移到带有噪音的手写文档上并非易事，其性能下降了三分之二。从不相关的结构化添加物（例如方格纸）到非结构化的部分遮挡物（例如不透明），手写文档环境中的噪音可能以多种方式表现出来。咖啡渍和邮票。混淆算法作家识别解决方案的其他问题包括使用不同的写作工具，年龄和写作心理状态。拟议的工作探讨了去噪神经网络的培训，以帮助识别手写文档的作者。我们的算法在现有的干净的人工数据集上进行了人工训练，并人工添加了噪声，并且我们在委托的数据集上对其进行了评估，该数据集具有多样化但平衡的作家，书写工具和衬底（包括各种类型的噪音）的集合。使用提出的降噪算法，我们在嘈杂的手写文档的作者识别方面大大超越了现有技术。

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