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 - [2010-CVPR]Detecting Text in Natural Scenes with Stroke Width Transform [[Paper]](http://www.math.tau.ac.il/~turkel/imagepapers/text_detection.pdf)
 
 ### Scene Text Recognition
-- [2017-arvix ] Full-Page TextRecognition : Learning Where to Start and When to Stop[  href="https://arxiv.org/pdf/1704.08628.pdf
+- [2017-arvix ] Full-Page TextRecognition : Learning Where to Start and When to Stop https://arxiv.org/pdf/1704.08628.pdf
 - [2016-AAAI]Reading Scene Text in Deep Convolutional Sequences [[Paper]](http://whuang.org/papers/phe2016_aaai.pdf)
 - [2016-IJCV]Reading Text in the Wild with Convolutional Neural Networks [[Paper]](http://arxiv.org/abs/1412.1842) http://zeus.robots.ox.ac.uk/textsearch/#/search/ http://www.robots.ox.ac.uk/~vgg/research/text
 - [2016-CVPR]Recursive Recurrent Nets with Attention Modeling for OCR in the Wild [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Lee_Recursive_Recurrent_Nets_CVPR_2016_paper.pdf)
@@ -127,56 +127,33 @@
 - [Applying OCR Technology for Receipt Recognition](http://rnd.azoft.com/applying-ocr-technology-receipt-recognition/)
 - [Convolutional Neural Networks for Object(Car License) Detection](http://rnd.azoft.com/convolutional-neural-networks-object-detection/)
 - [Extracting text from an image using Ocropus](http://www.danvk.org/2015/01/09/extracting-text-from-an-image-using-ocropus.html)
-- [Number plate recognition with Tensorflow](http://matthewearl.github.io/2016/05/06/cnn-anpr/) [`github`](https://github.com/matthewearl/deep-anpr)
-- [Using deep learning to break a Captcha system](https://deepmlblog.wordpress.com/2016/01/03/how-to-break-a-captcha-system/) [`report`](http://web.stanford.edu/~jurafsky/burszstein_2010_captcha.pdf) [`github`](https://github.com/arunpatala/captcha)
-- [Breaking reddit captcha with 96% accuracy](https://deepmlblog.wordpress.com/2016/01/05/breaking-reddit-captcha-with-96-accuracy/) [`github`](https://github.com/arunpatala/reddit.captcha)
+- [Number plate recognition with Tensorflow](http://matthewearl.github.io/2016/05/06/cnn-anpr/) [[github]](https://github.com/matthewearl/deep-anpr)
+- [Using deep learning to break a Captcha system](https://deepmlblog.wordpress.com/2016/01/03/how-to-break-a-captcha-system/) [`report`](http://web.stanford.edu/~jurafsky/burszstein_2010_captcha.pdf) [[github]](https://github.com/arunpatala/captcha)
+- [Breaking reddit captcha with 96% accuracy](https://deepmlblog.wordpress.com/2016/01/05/breaking-reddit-captcha-with-96-accuracy/) [[github]](https://github.com/arunpatala/reddit.captcha)
 
 
 ### Open Resources Code
 - Tesseract c++ based tools for documents analysis and OCR [[code]](https://github.com/tesseract-ocr/tesseract)
-- Ocropy</span><span style="background:#F2EDE1">: Python-based tools for document analysis and OCR [  href="https://github.com/tmbdev/ocropy"><span style="background:#F2EDE1">code</span></a><span style="background:#F2EDE1">]</span></p>
-- CLSTM</span><span style="background:#F2EDE1"> : A small C&#43;&#43; implementation of LSTM networks,focused on OCR [  href="https://github.com/tmbdev/clstm"><span style="background:#F2EDE1">code</span></a><span style="background:#F2EDE1">]</span></p>
-- Convolutional Recurrent Neural Network,</span><span style="background:#F2EDE1">Torch7 based [  href="https://github.com/bgshih/crnn"><span style="background:#F2EDE1">code</span></a><span style="background:#F2EDE1">]</span></p>
-- Attention-OCR</span><span style="background:#F2EDE1">: Visual Attention based OCR [  href="https://github.com/da03/Attention-OCR"><span style="background:#F2EDE1">code</span></a><span style="background:#F2EDE1">]</span></p>
-- Umaru</span><span style="background:#F2EDE1">: An OCR-system based on torch using the technique of LSTM/GRU-RNN, CTC and referred to the works of rnnlib and clstm [  href="https://github.com/edward-zhu/umaru"><span style="background:#F2EDE1">code</span></a><span style="background:#F2EDE1">]</span></p>
+- Ocropy</span><span style="background:#F2EDE1">: Python-based tools for document analysis and OCR https://github.com/tmbdev/ocropy
+- CLSTM A small implementation of LSTM networks,focused on OCR https://github.com/tmbdev/clstm
+- Convolutional Recurrent Neural Network Torch7 https://github.com/bgshih/crnn
+- Attention-OCR Visual Attention based OCR https://github.com/da03/Attention-OCR
+- Umaru: An OCR-system based on torch using the technique of LSTM/GRU-RNN, CTC and referred to the works of rnnlib and clstm https://github.com/edward-zhu/umaru
 - AKSHAYUBHAT/DeepVideoAnalytics  (CTPN+CRNN) [code](https://github.com/AKSHAYUBHAT/DeepVideoAnalytics/tree/master/notebooks/OCR)
 - ankush-me/SynthText [code](https://github.com/ankush-me/SynthText)
 - JarveeLee/SynthText_Chinese_version [code](https://github.com/JarveeLee/SynthText_Chinese_version)
-### Other
-- DeepFont:Identify Your Font from An Image [[Paper]](http://arxiv.org/abs/1507.03196)
-- Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks [[Paper]](http://arxiv.org/abs/1604.00974)
-- End-to-End Interpretation of the French Street Name Signs Dataset [  href="http://link.springer.com/chapter/10.1007%2F978-3-319-46604-0_30"> 
- [  href="https://github.com/tensorflow/models/tree/master/street"> </span></p>
-- Extracting text from an image using Ocropus [  href="http://www.danvk.org/2015/01/09/extracting-text-from-an-image-using-ocropus.html"><span style="font-weight:bold; background:#F2EDE1">blog</span></a><span style="font-weight:bold; background:#F2EDE1">]</span></p>
 
 #### Hand Writing Recognition</strong>
-- [2016-arXiv]Drawingand Recognizing Chinese Characters with Recurrent Neural Network [  href="https://arxiv.org/abs/1606.06539
-- Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition [  href="https://arxiv.org/abs/1610.02616
-- Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition [  href="https://arxiv.org/abs/1610.04057
-- High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps [  href="http://arxiv.org/abs/1505.04925"> 
-- DeepHCCR:Offline Handwritten Chinese Character Recognition based on GoogLeNet and AlexNet (With CaffeModel) [  href="https://github.com/chongyangtao/DeepHCCR"> </span></p>
-- Scan,Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTMAttention [  href="http://arxiv.org/abs/1604.03286
-- MLPaint:the Real-Time Handwritten Digit Recognizer [  href="http://blog.mldb.ai/blog/posts/2016/09/mlpaint/"><span style="font-weight:bold; background:#F2EDE1">blog</span></a><span style="font-weight:bold; background:#F2EDE1">][  href="https://github.com/mldbai/mlpaint"> [  href="https://docs.mldb.ai/ipy/notebooks/_demos/_latest/Image%20Processing%20with%20Convolutions.html"><span style="font-weight:bold; background:#F2EDE1">demo</span></a><span style="font-weight:bold; background:#F2EDE1">]</span></p>
-- caffe-ocr: OCR with caffe deep learning framework [  href="https://github.com/pannous/caffe-ocr"><span lang="en-US" style="font-weight:bold; background:#F2EDE1">code</span></a><span lang="en-US" style="font-weight:bold; background:#F2EDE1">]
- (</span><span lang="zh-CN" style="font-weight:bold; background:#F2EDE1">单字分类器</span><span lang="en-US" style="font-weight:bold; background:#F2EDE1">)</span></p>
+- [2016-arXiv]Drawingand Recognizing Chinese Characters with Recurrent Neural Network  https://arxiv.org/abs/1606.06539
+- Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition  https://arxiv.org/abs/1610.02616
+- Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition  https://arxiv.org/abs/1610.04057
+- High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature Maps  http://arxiv.org/abs/1505.04925"> 
+- DeepHCCR:Offline Handwritten Chinese Character Recognition based on GoogLeNet and AlexNet (With CaffeModel)  https://github.com/chongyangtao/DeepHCCR"> </span></p>
+- Scan,Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTMAttention  http://arxiv.org/abs/1604.03286
+- MLPaint:the Real-Time Handwritten Digit Recognizer http://blog.mldb.ai/blog/posts/2016/09/mlpaint/
+- caffe-ocr: OCR with caffe deep learning framework https://github.com/pannous/caffe-ocr
 
 ####  Licence Tag Recognition
-- ReadingCar License Plates Using Deep Convolutional Neural Networks and LSTMs ; [  href="
-- Numberplate recognition with Tensorflow [  href="http://matthewearl.github.io/2016/05/06/cnn-anpr/"><span style="font-weight:bold; background:#F2EDE1">blog</span></a><span style="font-weight:bold; background:#F2EDE1">]
-- end-to-end-for-plate-recognition[  href="https://github.com/szad670401/end-to-end-for-chinese-plate-recognition"> </span></p>
-- ApplyingOCR Technology for Receipt Recognition[  href="http://rnd.azoft.com/applying-ocr-technology-receipt-recognition/"><span style="font-weight:bold; background:#F2EDE1">blog</span></a><span style="font-weight:bold; background:#F2EDE1">][  href="http://pan.baidu.com/s/1qXQBQiC"><span style="font-weight:bold; background:#F2EDE1">mirror</span></a><span style="font-weight:bold; background:#F2EDE1">]</span></p>
-
-#### Verification Code
-- [2017-Arvix]Using Synthetic Data to Train NeuralNetworks is Model-Based Reasoning[  href="https://arxiv.org/pdf/1703.00868.pdf"> </span><br>
-- Using deep learning to break a Captcha system [  href="https://deepmlblog.wordpress.com/2016/01/03/how-to-break-a-captcha-system/"><span style="font-weight:bold; background:#F2EDE1">blog</span></a><span style="font-weight:bold; background:#F2EDE1">]
-- Breakingreddit captcha with 96% accuracy [  href="https://deepmlblog.wordpress.com/2016/01/05/breaking-reddit-captcha-with-96-accuracy/"><span style="font-weight:bold; background:#F2EDE1">blog</span></a><span style="font-weight:bold; background:#F2EDE1">]
-- I'm not a human: Breaking the Google reCAPTCHA (https://www.blackhat.com/docs/asia-16/materials/asia-16-Sivakorn-Im-Not-a-Human-Breaking-the-Google-reCAPTCHA-wp.pdf)
-- NeuralNet CAPTCHA Cracker [  href="http://www.cs.sjsu.edu/faculty/pollett/masters/Semesters/Spring15/geetika/CS298%20Slides%20-%20PDF"><span style="font-weight:bold; background:#F2EDE1">slides</span></a><span style="font-weight:bold; background:#F2EDE1">]
- [  href="https://github.com/bgeetika/Captcha-Decoder">  [  href="http://cp-training.appspot.com/"><span style="font-weight:bold; background:#F2EDE1">demo</span></a><span style="font-weight:bold; background:#F2EDE1">]</span></p>
-- Recurrentneural networks for decoding CAPTCHAS [  href="https://deepmlblog.wordpress.com/2016/01/12/recurrent-neural-networks-for-decoding-captchas/"><span style="font-weight:bold; background:#F2EDE1">blog</span></a><span style="font-weight:bold; background:#F2EDE1">]
- [  href="http://sourceforge.net/projects/simplecaptcha/">  [  href="http://simplecaptcha.sourceforge.net/"><span style="font-weight:bold; background:#F2EDE1">demo</span></a><span style="font-weight:bold; background:#F2EDE1">]</span></p>
-- Readingirctc captchas with 95% accuracy using deep learning [  href="https://github.com/arunpatala/captcha.irctc"> </span></p>
-- End-to-End</span><span lang="en-US" style="font-weight:bold; background:#F2EDE1">OCR</span><span lang="zh-CN" style="font-weight:bold; background:#F2EDE1">：based on </span><span lang="en-US" style="font-weight:bold; background:#F2EDE1">CNN</span><span lang="en-US" style="font-weight:bold; background:#F2EDE1">
- [  href="http://blog.xlvector.net/2016-05/mxnet-ocr-cnn/"><span lang="en-US" style="font-weight:bold; background:#F2EDE1">blog</span></a><span lang="en-US" style="font-weight:bold; background:#F2EDE1">]</span></p>
-- IAm Robot: (Deep) Learning to Break Semantic Image CAPTCHAs [  href="http://www.cs.columbia.edu/~polakis/papers/sivakorn_eurosp16.pdf
-
+- ReadingCar License Plates Using Deep Convolutional Neural Networks and LSTMs 
+- Numberplate recognition with Tensorflow http://matthewearl.github.io/2016/05/06/cnn-anpr/
+- end-to-end-for-plate-recognition  href="https://github.com/szad670401/end-to-end-for-chinese-plate-recognitionbhttp://rnd.azoft.com/applying-ocr-technology-receipt-recognition/