文件名称：Python-图片识别发票识别
文件大小：640MB
文件格式：ZIP
更新时间：2022-08-29 22:38:44
Python开发-图片处理
此项目用于对中国购车发票进行内容识别,目前完成的是身份证,vin,发动机号,价格的识别 提供了展示的demo页,以及提供了传入文件,路径,base64码的多种方式调用的api,返回识别出来的json数据
【文件预览】：
PJ_PREDICT_IMG-master
----train()
--------keras_predict.py(1KB)
--------predictapi.py(11KB)
--------keras_alphabet_upper.py(3KB)
--------keras_number_0502.py(4KB)
--------invalueimg_clear.py(1KB)
--------keras_all.py(5KB)
--------keras_number_X.py(4KB)
--------main.py(2KB)
--------keras_alphabet_test3.py(3KB)
--------__init__.py(0B)
--------keras_upper_number_0505.py(4KB)
--------keras_upper_number.py(4KB)
--------keras_number_X_new.py(4KB)
--------numberX.py(4KB)
--------keras_number_X_qianyi.py(4KB)
--------dataload.py(9KB)
--------keras_number_upperX.py(4KB)
----api_common()
--------utils()
----wordlist_mono_clean.txt(673KB)
----api_invoice()
--------service()
--------utils()
--------test.py(15KB)
--------main.py(8KB)
--------dao()
--------config()
--------README.md(2KB)
--------api_test.py(1KB)
--------demo.html(3KB)
----trainexample()
--------github_mnist.py(2KB)
----data_check()
--------predict_result.py(7KB)
--------main.py(4KB)
--------predict_result.html(6KB)
--------demo.html(4KB)
----traintest()
--------keras_number.py(3KB)
----train_dataset()
--------numberAndUpper.tar.gz(38.14MB)
--------numberAndX.tar.gz(18.44MB)
----.idea()
--------vcs.xml(180B)
----README.md(6KB)
----data()
--------invoice()
----creatTrainDataSet()
--------createTrainDateSet.py(17KB)
--------main.sh(844B)
--------test.py(2KB)
--------dataAugmentation.py(1KB)
--------main.py(5KB)
--------__init__.py(0B)
--------mydataset.py(2KB)
--------readme.txt(1KB)
--------createOriginalImage.py(3KB)
----.gitignore(78B)
----wordlist_bi_clean.txt(998KB)
----trainmodel()
--------weights_number_X_3.h5(58MB)
--------architecture_number_X1.json(6KB)
--------weights_number_X2.h5(44.75MB)
--------architecture_number_X2.json(6KB)
--------architecture_upper_number_1.json(6KB)
--------architecture_number_test.json(5KB)
--------weights_upper_number_0.h5(54.79MB)
--------architecture_number.json(5KB)
--------weights_number_X1_0.h5(86.98MB)
--------weights_number_X_1.h5(58MB)
--------architecture_number_X0.json(6KB)
--------architecture_number_X1_0.json(6KB)
--------architecture_upper_number.json(6KB)
--------weights_upper_number_1.h5(54.79MB)
--------weights_number_test.h5(8.06MB)
--------weights_number_X_0.h5(58MB)
--------weights_number.h5(8.13MB)
--------weights_number_X1.h5(44.75MB)
--------readme.txt(290B)
--------architecture_upper_number_0.json(6KB)
--------architecture_number_X2_1.json(6KB)
--------weights_number_X0.h5(58MB)
--------weights_number_X2_1.h5(44.75MB)
--------weights_upper_number.h5(54.79MB)
----img_process()
--------utils()
--------main.py(93B)