[论文解读] Segmentation-free Vehicle License Plate Recognition using ConvNet-RNN
一种无分割的VLPR方法，使用ConvNet进行特征提取，RNN进行序列建模，对完整车牌图像端到端处理，且性能优于滑动窗口方法。
While vehicle license plate recognition (VLPR) is usually done with a sliding window approach, it can have limited performance on datasets with characters that are of variable width. This can be solved by hand-crafting algorithms to prescale the characters. While this approach can work fairly well, the recognizer is only aware of the pixels within each detector window, and fails to account for other contextual information that might be present in other parts of the image. A sliding window approach also requires training data in the form of presegmented characters, which can be more difficult to obtain. In this paper, we propose a unified ConvNet-RNN model to recognize real-world captured license plate photographs. By using a Convolutional Neural Network (ConvNet) to perform feature extraction and using a Recurrent Neural Network (RNN) for sequencing, we address the problem of sliding window approaches being unable to access the context of the entire image by feeding the entire image as input to the ConvNet. This has the added benefit of being able to perform end-to-end training of the entire model on labelled, full license plate images. Experimental results comparing the ConvNet-RNN architecture to a sliding window-based approach shows that the ConvNet-RNN architecture performs significantly better.
研究动机与目标
- 在字符宽度可变的真实世界数据集上推动VLPR的应用。
- 克服依赖预分割字符的滑动窗口方法的局限性。
- 提出一种端到端的ConvNet-RNN架构，使用整张图像作为输入进行识别。
提出的方法
- 使用卷积神经网络（ConvNet）从完整的车牌图像中提取特征。
- 在提取的特征上使用循环神经网络（RNN）进行序列建模。
- 实现对带标注的完整车牌图像进行端到端训练的整个ConvNet-RNN模型。
- 避免对预分割字符或手工预缩放字符的依赖。
实验结果
研究问题
- RQ1ConvNet-RNN是否能在没有预分割组件的情况下识别车牌字符？
- RQ2带上下文对整图处理是否比滑动窗口检测在识别上更优？
- RQ3在真实世界的车牌照片上进行端到端训练的分割无关VLPR是否可行？
主要发现
- ConvNet-RNN架构处理整个车牌图像，使端到端训练成为可能。
- 与滑动窗口方法相比，ConvNet-RNN在所研究的数据上取得显著更好的性能。
- 利用整张图像的上下文信息可以提升识别效果，相较于传统方法。
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