Abstract
Reading irregular text of arbitrary shape in natural scene images is still a challenging problem. Many existing approaches incorporate sophisticated network structures to handle various shapes, use extra annotations for stronger supervision, or employ hard-to-train recurrent neural networks for sequence modeling. In this work, we propose a simple yet robust approach for irregular text recognition. With no need to convert input images to sequence representations, we directly connect two-dimensional CNN features to an attention-based sequence decoder. As no recurrent module is adopted, our model can be trained in parallel. It achieves 3x to 18x acceleration to backward pass and 2x to 12x acceleration to forward pass, compared with the RNN counterparts. The proposed model is trained with only word-level annotations. With this simple design, our method achieves state-of-the-art or competitive recognition performance on the evaluated regular and irregular scene text benchmark datasets. Furthermore, we show that the recognition performance does not significantly degrade with inaccurate bounding boxes. This is desirable for tasks of end-to-end text detection and recognition: robust recognition performance can still be achieved with an inaccurate text detector. We will release the code.
Abstract (translated)
在自然场景图像中读取任意形状的不规则文本仍然是一个具有挑战性的问题。许多现有的方法包括复杂的网络结构来处理各种形状，使用额外的注释来加强监督，或者使用难以训练的递归神经网络来进行序列建模。在这项工作中，我们提出了一个简单而强大的方法，不规则文本识别。由于不需要将输入图像转换为序列表示，我们直接将二维CNN功能连接到基于注意的序列解码器。由于不采用重复模块，我们的模型可以并行训练。它实现了3倍到18倍的加速度向后通过和2倍到12倍的加速度向前通过，与RNN对应。该模型只使用单词级注释进行训练。通过这种简单的设计，我们的方法在评估的规则和不规则场景文本基准数据集上实现了最先进的或具有竞争力的识别性能。此外，我们还发现不准确的边界框不会显著降低识别性能。这对于端到端的文本检测和识别任务是可取的：使用不准确的文本检测器仍然可以获得强大的识别性能。我们将发布代码。
URL
https://arxiv.org/abs/1904.01375
