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Deep Learning Approach for Receipt Recognition

机译:收据识别的深度学习方法

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Inspired by the recent successes of deep learning on Computer Vision and Natural Language Processing, we present a deep learning approach for recognizing scanned receipts. The recognition system has two main modules: text detection based on Connectionist Text Proposal Network and text recognition based on Attention-based Encoder-Decoder. We also proposed preprocessing to extract receipt area and OCR verification to ignore handwriting. The experiments on the dataset of the Robust Reading Challenge on Scanned Receipts OCR and Information Extraction 2019 demonstrate that the accuracies were improved by integrating the pre-processing and the OCR verification. Our recognition system achieved 71.9% of the Fl score for detection and recognition task.
机译:受到最近在计算机视觉和自然语言处理方面进行深度学习的成功的启发,我们提出了一种用于识别扫描收据的深度学习方法。识别系统具有两个主要模块:基于Connectionist文本提议网络的文本检测和基于基于注意力的Encoder-Decoder的文本识别。我们还建议进行预处理以提取收据区域,并提出OCR验证以忽略手写。对扫描的收据OCR和信息提取的鲁棒阅读挑战2019的数据集进行的实验表明,通过整合预处理和OCR验证可以提高准确性。我们的识别系统在检测和识别任务中获得了Fl分数的71.9%。

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