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Full-Page Text Recognition: Learning Where to Start and When to Stop

机译:整页文本识别:学习从哪里开始和何时停止

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摘要

Text line detection and localization is a crucial step for full page documentanalysis, but still suffers from heterogeneity of real life documents. In thispaper, we present a new approach for full page text recognition. Localizationof the text lines is based on regressions with Fully Convolutional NeuralNetworks and Multidimensional Long Short-Term Memory as contextual layers. Inorder to increase the efficiency of this localization method, only the positionof the left side of the text lines are predicted. The text recognizer is thenin charge of predicting the end of the text to recognize. This method has showngood results for full page text recognition on the highly heterogeneous Maurdordataset.
机译:文本行检测和本地化是整页文档分析的关键步骤,但仍然遭受现实文档异质性的困扰。在本文中,我们提出了一种用于全页文本识别的新方法。文本行的本地化基于完全卷积神经网络和多维长期短期记忆作为上下文层的回归。为了提高这种定位方法的效率,仅预测了文本行左侧的位置。然后,文本识别器负责预测要识别的文本的结尾。对于高度异构的Maurdor数据集,该方法对于全页文本识别已显示出良好的结果。

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