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"classifiers": [
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"description": "# Taiwan Train Verification Code 2 text ( \u53f0\u9435\u9a57\u8b49\u78bc\u8f49\u6587\u5b57 )\n\n\n[](https://github.com/linsamtw/TaiwanTrainVerificationCode2text/blob/master/LICENSE)\n[](https://badge.fury.io/py/TaiwanTrainVerificationCode2text)\n\n-------------------\n## introduce\n\u63d0\u4f9b\u5404\u4f4d\u958b\u767c\u7a0b\u5f0f\u8a02\u7968\uff0c\u5176\u4e2d\u9a57\u8b49\u78bc\u7834\u89e3\u90e8\u5206\u7684\u65b9\u6cd5\uff0c\u53ef\u76f4\u63a5\u4f7f\u7528\uff0c\u4e0d\u9808\u518d\u56de\u50b3 image \u7528\u4eba\u5de5\u65b9\u5f0f\u8fa8\u8b58\u3002
\n\u6b64 package \u4f7f\u7528 keras & Tensorflow \u5efa\u6a21&\u9810\u6e2c\uff0c\u9700\u8981\u5b89\u88dd\u76f8\u4f9d package\u3002\n\nmodel test data \u6e96\u78ba\u7387\u7d04\u70ba 88%\uff0c\u4f7f\u7528 10 \u842c\u5f35\u5716\u9032\u884c training\u3002\n\n-------------------\n\n\tpip3 install TaiwanTrainVerificationCode2text\n \ncv2 \u6bd4\u8f03\u96e3\u88dd\uff0c\u4ee5\u4e0b\u63d0\u4f9b\u5b89\u88dd\u65b9\u6cd5\n\n conda install -c menpo opencv\n\t# \u4f60\u9084\u9700\u8981\u9019\u500b\n pip3 install h5py\n\n---------------------------------\n\n## demo\n\tinput \n\n\n\toutput\nWNBA8S\n\n--------------------\n\n## exmaple \n\n import os\n from TaiwanTrainVerificationCode2text import verification_code2text\n from TaiwanTrainVerificationCode2text import work_vcode \n from TaiwanTrainVerificationCode2text import download \n import TaiwanTrainVerificationCode2text\n PATH = TaiwanTrainVerificationCode2text.__path__[0]\n import cv2\n import matplotlib.pyplot as plt\n import random\n\n\t# \u4e0b\u8f09\u6211 train \u597d\u7684 weight\uff0cttf \u662f\u9a57\u8b49\u78bc\u5b57\u5f62\uff0c\u7528\u65bc\u4ee5\u4e0b\u751f\u6210\u6a21\u64ec\u9a57\u8b49\u78bc\n download.weight()\n download.ttf()\n\t# \u751f\u6210\u6a21\u64ec\u9a57\u8b49\u78bc\n work_vcode.work_vcode_fun(10,'test_data',5)\n work_vcode.work_vcode_fun(10,'test_data',6)\n file_path = '{}/{}/'.format(PATH,'test_data')\n train_image_path = [file_path + i for i in os.listdir(file_path+'/')]\n\t# \u96a8\u6a5f\u53d6\u4e00\u500b\u7576\u4f5c demo\n image_name = train_image_path[random.sample( range(len(train_image_path)) ,1)[0]]\n\t# \u8b80\u53d6\u5716\u7247\n image = cv2.imread(image_name)\n # \u756b\u5716\n plt.imshow(image)\n\t# \u8fa8\u8b58\uff0c\u9a57\u8b49\u78bc\u8f49\u6587\u5b57\n text = verification_code2text.main(image)\n # \u5370\u51fa\u6700\u5f8c\u7d50\u679c\n print(text)\n\n\u6700\u5f8c\u7d50\u679c\u5c31\u6703\u985e\u4f3c demo \uff0c\n\n-------------------------------\n\n\u5982\u679c\u60f3\u81ea\u5df1 train\uff0c\u53ef\u4ee5\u4f7f\u7528\n\n[build_verification_code_cnn_model.py](https://github.com/linsamtw/TaiwanTrainVerificationCode2text/blob/master/build_verification_code_cnn_model.py)\n\n\u7a0d\u5fae\u4ecb\u7d39\u4e3b\u8981 code\n\n def main():\n import work_vcode \n #import time\n # \u56e0\u70ba\u53f0\u9435\u9a57\u6574\u78bc\u662f 5~6 \u96a8\u6a5f\uff0c\u56e0\u6b64\u5fc5\u9808\u751f\u6210 5 \u78bc\u9a57\u8b49\u78bc& 6 \u78bc\u9a57\u8b49\u78bc\n # 500 \u662f data \u6578\u91cf\uff0c\u5efa\u8b70\u6578\u5b57\u70ba30000\uff0c500 \u53ea\u662f demo\n work_vcode.work_vcode_fun(500,'train_data',5)\n work_vcode.work_vcode_fun(500,'train_data',6)\n\t\t# \u751f\u6210 test data\uff0c\u53ef\u6839\u64da\u81ea\u5df1\u559c\u597d\u8abf\u6574 data \u6578\u91cf\n work_vcode.work_vcode_fun(100,'test_data',5)\n work_vcode.work_vcode_fun(100,'test_data',6)\n self = build_verification_code_cnn_model()\n # \u5efa\u6a21\uff0c\u6700\u5f8c weight \u6703\u5b58\u653e\u5728 package_path/cnn_weight/verificatioin_code.h5\n self.build_model_process() \n \n train \u597d\u5f8c\uff0c\u53ef\u518d\u4f7f\u7528\u4ee5\u4e0a example code\uff0c\u6703\u8b80\u53d6\u4f60 train \u597d\u7684 weight\u3002\n\n--------------------------\n\n## \u65b9\u6cd5\n\u7531\u65bc\u9a57\u8b49\u78bc\u662f26\u500b\u82f1\u6587\u5b57\u642d\u914d10\u500b\u6578\u5b57\uff0c\u518d\u52a0\u4e0a\u96a8\u6a5f 5~6 \u78bc\uff0c\u6211\u5c07 class \u5206\u6210 26 + 10 + null\uff0c37\u985e\uff0c\u4e0b\u53bb\u505a\u5206\u985e\u3002
\nnull \u4ee3\u8868\u6c92\u6709\u6b64\u6587\u5b57\uff0c \u6211\u5c07 NN \u7d50\u69cb\u4e2d\u7684 output \u8a2d\u8a08\u70ba 6 dimension\uff0c\u5c075\u78bc\u4e5f\u770b\u62106\u78bc\uff0c\u53ea\u662f\u6700\u5f8c\u4e00\u500b\u662f null\uff0c\u85c9\u6b64\u540c\u6642\u8655\u7406 5 or 6 \u78bc\u554f\u984c\u3002
\n\n----------------\n## future\n\u9762\u81e8\u5230\u6700\u5927\u7684\u554f\u984c\u662f\uff0cnull \u4f54\u6bd4\u4f8b\u592a\u5927\uff0c\u4ee5\u4e0a\u9762\u751f\u6210 data \u7684 code \u70ba\u4f8b\n\n work_vcode.work_vcode_fun(500,'train_data',5)\n work_vcode.work_vcode_fun(500,'train_data',6)\n\u5148\u4e0d\u7ba1\u82f1\u6587&\u6578\u5b57\u6578\u91cf\uff0c\u6709\u4e00\u534a\u7684 class \u662f null\uff0c\u56e0\u6b64\u5728\u5206\u985e\u554f\u984c\u4e0a\uff0c\u6703\u9020\u6210\u56b4\u91cd\u7684 imbalance \u554f\u984c\uff0c\u5373\u4f7f data \u589e\u52a0\u4e5f\u4e0d\u6703\u6539\u5584\u554f\u984c\uff0c\u672a\u4f86\u5c07\u642d\u914d object detection\uff0c\u6539\u5584\u6e96\u78ba\u7387\u3002\n\n-----------------------\n\u5982\u6709\u554f\u984c\uff0c\u53ef\u5bc4\u4fe1\u7d66\u6211 or \u7559\u8a00\u5728 issues\u3002\n\nemail : linsam.tw.github@gmail.com",
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