README.md · 静听/CRNN - Gitee.com

10月26日，北京源创会，聊聊高性能计算与大模型推理

加入 Gitee

与超过 1200万开发者一起发现、参与优秀开源项目，私有仓库也完全免费：）

克隆/下载

README.md 1.75 KB

**This repository has been archived. Please see all the other (much better) implementations around GitHub**

# CRNN

A TensorFlow implementation of https://github.com/bgshih/crnn

## But what is a CRNN?

It is a Convolutional Recurrent Neural Network that can be used as an OCR

## Requirements

- Tensorflow (tested with 1.8) `pip3 install tensorflow`
- Scipy `pip3 install scipy`

## What training data was used?

All training data (200 000 examples) were generated using my other project https://github.com/Belval/TextRecognitionDataGenerator

To do the same, simply install that project with pip (`pip install trdg`) and do `trdg -c 200000 -w 1 -t 8`. `-t` should be your processor thread count.

## Pretrained model

Available in CRNN/save. Use `python3 run.py -ex ../data/test --test --restore` to test.

## Specify charset

You can specify charset to include only numbers `python run.py --train -ex ../data/test -it 50000 -cs 0123456789`

## Results

It works but is a suboptimal solution for OCR in its current form as it makes some mistakes. Do note that I used a bigger char vector than the paper.

For fun, here are a list of words with their prediction:

| Ground truth 	| Prediction 	| Image 	|
|--------------	|------------	|-------	|
| retardates 	| retardates 	| ![1](samples/1.jpg "1") 	|
| pay-roller 	| poy-roler 	| ![2](samples/2.jpg "2") 	|
| rhizopodist | rhizospodist |  ![3](samples/3.jpg "3")	|
| theriacas | trenagas |  ![4](samples/4.jpg "4")	|
| semantically | semanticaly |  ![5](samples/5.jpg "5")	|
| dualistic | duaistic |  ![6](samples/6.jpg "6")	|
| high-flying | highi-fling | ![7](samples/7.jpg "7") 	|
| grossify | grsity | ![8](samples/8.jpg "8") 	|
| scutular | scutular |  ![9](samples/9.jpg "9")	|
| crispened | crispened | ![10](samples/10.jpg "10") 	|

一键复制编辑原始数据按行查看历史

提交于 2020-06-10 19:35 . Update README.md

**This repository has been archived. Please see all the other (much better) implementations around GitHub**

# CRNN

A TensorFlow implementation of https://github.com/bgshih/crnn

## But what is a CRNN?

It is a Convolutional Recurrent Neural Network that can be used as an OCR

## Requirements

- Tensorflow (tested with 1.8) `pip3 install tensorflow`
- Scipy `pip3 install scipy`

## What training data was used?

All training data (200 000 examples) were generated using my other project https://github.com/Belval/TextRecognitionDataGenerator

To do the same, simply install that project with pip (`pip install trdg`) and do `trdg -c 200000 -w 1 -t 8`. `-t` should be your processor thread count.

## Pretrained model

Available in CRNN/save. Use `python3 run.py -ex ../data/test --test --restore` to test.

## Specify charset

You can specify charset to include only numbers `python run.py --train -ex ../data/test -it 50000 -cs 0123456789`

## Results

It works but is a suboptimal solution for OCR in its current form as it makes some mistakes. Do note that I used a bigger char vector than the paper.

For fun, here are a list of words with their prediction:

| Ground truth 	| Prediction 	| Image 	|
|--------------	|------------	|-------	|
| retardates 	| retardates 	| ![1](samples/1.jpg "1") 	|
| pay-roller 	| poy-roler 	| ![2](samples/2.jpg "2") 	|
| rhizopodist | rhizospodist |  ![3](samples/3.jpg "3")	|
| theriacas | trenagas |  ![4](samples/4.jpg "4")	|
| semantically | semanticaly |  ![5](samples/5.jpg "5")	|
| dualistic | duaistic |  ![6](samples/6.jpg "6")	|
| high-flying | highi-fling | ![7](samples/7.jpg "7") 	|
| grossify | grsity | ![8](samples/8.jpg "8") 	|
| scutular | scutular |  ![9](samples/9.jpg "9")	|
| crispened | crispened | ![10](samples/10.jpg "10") 	|

马建仓 AI 助手

尝试更多

代码解读

代码找茬

代码优化

1

https://gitee.com/advancer-debug/CRNN.git

git@gitee.com:advancer-debug/CRNN.git

advancer-debug

CRNN

CRNN

master