Pytorch implementation for "Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter".

Overview

Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter

This is a pytorch-based implementation for paper Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter.

Due to the company's code confidentiality requirements, we only release the code of ExCTC on the IAM dataset.

Personally, I dont't think it is a thorough work, but I hope this idea is useful.

Requirements

Data Preparation

We re-crop the lines from IAM full-page images. This operation is to remove the edge phenomenon in the official line images.

Official image:

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Re-cropped image:

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These re-cropped images are used as training set.

Training

Trained parameter: https://drive.google.com/drive/folders/1rXJ9at9erPN6v5nPAOGWsMWFUiwBq8D6?usp=sharing

Crop the training images (data/crop_images.py) and modify the path in configuration files (cfgs.py). Then

	python main.py

A simple demo:

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Ackowledgement

We use the augmentation toolkit released by RubanSeven to train the network.

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Comments
  • 关于concentrate loss的作用

    关于concentrate loss的作用

    作者,你好。我在看您代码的时候发现里面有一项concentrate loss,这个在论文中最后的优化函数中似乎没有提到,在实际训练中发现concentrate loss在一定的轮次加入训练时,在段落级别的效果会先下降,然后才会慢慢的提升。请问这项loss是训练时的trick呢,还是有其他的原因,加不加这项loss,对最终结果的影响大吗?希望能得到您的回复

    opened by milely 0
Owner
wangtianwei
wangtianwei
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