Hu, W., Cai, X., Hou, J., Yi, S., & Lin, Z. (AAAI2020). GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition. Retrieved from http://arxiv.org/abs/2002.01276

    1. Abstract: proposed the guided training of CTC (GTC), where CTC model learns a better alignment and feature representations from a more powerful attentional guidance，first attempt to apply graphs in scene text recognition and build sequence correlations by using GCN。<br />    Keywords: Graph Convolutional Network(GCN)    Connectionist Temporal Classification(CTC)


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    在论文中，作者认为当前识别算法都有局限性（精确率和推理时间之间的平衡关系）：

    1. attention-based methods不是并行解码，所以推理速度慢
    2. CTC-based methods 在 feature alignments and feature representations 容易出现误差，优化的没有attention-based 好，如对于label为AB，当在3 time-steps output时，有五种情况：‘A-B’ or ‘-AB’ or ‘AB-’ or ‘AAB’ or ‘ABB.
    3. 在CTC解码中存在blank和重复characters,因此作者认为相邻time steps中有supplementary features, 如果只考虑一个time step可能不完整，例如‘H’可能被识别为‘I’当只有字符的部分feature被考虑时。

    在上面基础上，作者提出以下这个东西：在training的时候，用attention-based的cross entropy去优化STN和feature extraction网络，用CTC loss 去优化GCN和CTC解码部分。在inference的时候，用CTC解码结合attention-based优化的feature extraction网络上的权重进行推理。作者还提出一个GCN模块去捕捉每个sequence slice的dependency进行合并提升模型的鲁棒性和准确率。

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    The STN, ResNet-CNN and the attentional guidance are solely trained with cross entropy loss, while the GCN+CTC decoder is trained with CTC loss.

    具体实现（主要是以下两块）：

    • Attentional Guidance
    • GCN+CTC Decoder
    • Conclusion