like
like
我们遇到的文字内容基本都是软件上的文字内容，很规整。
更新后的代码在哪里？
The image mentioned before is my own test image. And the model used is from your readme.md.
And I found the cost time is not stable, maybe 10s this time or 3s next time.
(fots) root@test-desktop:~/like/fots/FOTS_TF# python3.5 main_test.py --gpu_list='1' --test_data_path=test/ --checkpoint_path=checkpoints/SynthText_6_epochs/ make: Entering directory '/home/test/like/fots/FOTS_TF/lanms' make: 'adaptor.so' is up to date. make: Leaving directory '/home/test/like/fots/FOTS_TF/lanms' resnet_v1_50/block1 (?, ?, ?, 256) resnet_v1_50/block2 (?, ?, ?,...
中文的识别结果，为啥都是字母和数字： 404,407,710,414,709,472,402,464,h-1y51e7hi5 250,518,567,525,566,564,249,557,iric/-n? 220,594,488,585,489,604,220,613,M-senl 194,984,433,992,431,1027,193,1018,i2EJ1c- 284,807,587,819,585,853,282,842,isvE 73,1523,401,1536,399,1579,71,1566,Mes5E-F2t 63,1597,152,1599,148,1718,59,1715,a 242,1265,518,1255,519,1293,243,1303,-3he-7te 621,1416,870,1407,872,1459,623,1469,Netzxt 31,94,110,93,111,155,32,156,fow 965,1454,1032,1457,1030,1486,964,1483,2743 142,1584,428,1589,427,1625,142,1620,"nFhatElzsae 1025,1026,1077,1023,1079,1050,1027,1053,Xe 29,163,116,165,115,189,28,187,az 377,272,474,269,475,291,377,293,ivm 695,1254,834,1249,836,1285,697,1291,ges 575,1533,790,1528,791,1569,575,1574,cIEER-T 279,128,438,121,439,156,281,163,Heer -3,23,169,30,167,61,-4,54,Xhn 961,25,1061,20,1062,52,962,57,09:22 553,1590,687,1585,688,1617,555,1622,Frk 111,871,280,876,279,914,110,909,KL/E" 267,873,462,878,461,914,266,909,"FeEr'" 260,1420,408,1416,409,1463,261,1467,Ths 34,245,138,248,136,301,32,298,Jets 366,688,498,684,499,716,367,720,ey 722,1731,784,1729,785,1757,722,1759,ee 715,1115,792,1118,791,1151,714,1148,tit 763,240,863,242,862,293,762,291,Bgy...
明白了，已经很赞了。 如果我想做的是软件界面的文字识别，角度一般都是0度的，有什么快速的方法推荐么？
是的，就像上面的软件界面，基本都是水平文本。 谢谢，我看看。
尝试了一下，上面那张图需要2.5秒左右的时间。 还有就是这种两阶段的模型需要占用两个显卡，还是比较昂贵的哈。 还有其他方法么？
https://github.com/ouyanghuiyu/chineseocr_lite 这个我试了下，速度确实快，但是精度降低了很多，有点难以满足需求。 提高速度的思路，我理解是对模型进行了简化，FOTS会有FOTS_lite版本么？