论文标题
离线手写的中文文字识别带有卷积神经网络
Offline Handwritten Chinese Text Recognition with Convolutional Neural Networks
论文作者
论文摘要
基于深度学习的方法一直在不同的和多语言的场景中主导文本识别任务。离线手写的中文文本识别（HCTR）是最具挑战性的任务之一，因为它涉及数千个字符，变体写作样式和复杂的数据收集过程。最近，用于文本识别的无经常性架构似乎是其高度并行性和可比结果的竞争性。在本文中，我们仅使用卷积神经网络构建模型，并将CTC用作损失函数。为了减少过度拟合，我们在每个最大流动层之后应用辍学，并在线性层之前的最后一个掉速率。选择CASIA-HWDB数据库来调整和评估所提出的模型。以现有的文本样本为模板，我们随机选择孤立的字符样本来合成更多的文本样本进行训练。我们最终在ICDAR 2013竞赛集中达到了6.81％的字符错误率（CER），这是没有语言模型校正的最佳发布结果。
Deep learning based methods have been dominating the text recognition tasks in different and multilingual scenarios. The offline handwritten Chinese text recognition (HCTR) is one of the most challenging tasks because it involves thousands of characters, variant writing styles and complex data collection process. Recently, the recurrent-free architectures for text recognition appears to be competitive as its highly parallelism and comparable results. In this paper, we build the models using only the convolutional neural networks and use CTC as the loss function. To reduce the overfitting, we apply dropout after each max-pooling layer and with extreme high rate on the last one before the linear layer. The CASIA-HWDB database is selected to tune and evaluate the proposed models. With the existing text samples as templates, we randomly choose isolated character samples to synthesis more text samples for training. We finally achieve 6.81% character error rate (CER) on the ICDAR 2013 competition set, which is the best published result without language model correction.
