第一届西安交通大学人工智能实践大赛(2018AI实践大赛--图片文字识别)第一名;仅采用densenet识别图中文字

Overview

OCR

第一届西安交通大学人工智能实践大赛(2018AI实践大赛--图片文字识别)冠军

模型结果

该比赛计算每一个条目的f1score,取所有条目的平均,具体计算方式在这里。这里的计算方式不对一句话里的相同文字重复计算,故f1score比提交的最终结果低:

- train val
f1score 0.9911 0.9582
recall 0.9943 0.9574
precision 0.9894 0.9637

模型说明

  1. 模型

采用densenet结构,模型输入为(64×512)的图片,输出为(8×64×2159)的概率。

将图片划分为多个(8×8)的方格,在每个方格预测2159个字符的概率。

  1. Loss

将(8×64×2159)的概率沿着长宽方向取最大值,得到(2159)的概率,表示这张图片里有对应字符的概率。

balance: 对正例和负例分别计算loss,使得正例loss权重之和与负例loss权重之和相等,解决数据不平衡的问题。

hard-mining

  1. 文字检测 将(8×64×2159)的概率沿着宽方向取最大值,得到(64×2159)的概率。 沿着长方向一个个方格预测文字,然后连起来可得到一句完整的语句。

存在问题:两个连续的文字无法重复检测

下图是一个文字识别正确的示例:的长为半径作圆

下图是一个文字识别错误的示例:为10元;经粗加工后销售,每

文件目录

ocr
|
|--code
|
|--files
|	|
|	|--train.csv
|
|--data
	|
	|--dataset
	|	|
	|	|--train
	|	|
	|	|--test
	|
	|--result
	|	|
	|	|--test_result.csv
	|
	|--images		此文件夹放置任何图片均可,我放的celebA数据集用作pretrain

运行环境

Ubuntu16.04, python2.7, CUDA9.0

安装pytorch, 推荐版本: 0.2.0_3

pip install -r requirement.txt

下载数据

这里下载初赛、复赛数据、模型,合并训练集、测试集。

预处理

如果不更换数据集,不需要执行这一步。

如果更换其他数据集,一并更换 files/train.csv

cd code/preprocessing
python map_word_to_index.py
python analysis_dataset.py  

训练

cd code/ocr
python main.py

测试

f1score在0.9以下,lr=0.001,不使用hard-mining;

f1score在0.9以上,lr=0.0001,使用hard-mining;

生成的model保存在不同的文件夹里。

cd code/ocr
python main.py --phase test --resume  ../../data/models-small/densenet/eval-16-1/best_f1score.ckpt
Owner
尹畅
Ph.D. in CSE Research interests: deep learning, active learning, medical application
尹畅
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