add technical report url

README.md

@@ -4,9 +4,11 @@
 
 This is the **ranked No.1** tensorflow based scene text spotting algorithm on [__ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text__](https://rrc.cvc.uab.es/?ch=14) (Latin Only, Latin and Chinese), futhermore, the algorithm is also adopted in [__ICDAR2019 Robust Reading Challenge on Large-scale Street View Text with Partial Labeling__](https://rrc.cvc.uab.es/?ch=16) and [__ICDAR2019 Robust Reading Challenge on Reading Chinese Text on Signboard__](https://rrc.cvc.uab.es/?ch=12). 
 
-Scene text detection algorithm is modified from [__Tensorpack FasterRCNN__](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN), and we only open source code in this repository for scene text recognition. I upload ICDAR2019 ArT competition model to docker hub, please refer to [Docker](#Docker).
+Scene text detection algorithm is modified from [__Tensorpack FasterRCNN__](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN), and we only open source code in this repository for scene text recognition. I upload ICDAR2019 ArT competition model to docker hub, please refer to [Docker](#Docker). For more details, please refer to our [__arXiv technical report__](https://arxiv.org/abs/1912.04561).
 
-Note that our text recognition algorithm not only recognize Latin and Non-Latin characters, but also support horizontal and vertical text recognition in one model. It is convenient for multi-lingual arbitrary-shaped text recognition.
+Note that our text recognition algorithm not only recognizes Latin and Non-Latin characters, but also supports horizontal and vertical text recognition in one model. It is convenient for multi-lingual arbitrary-shaped text recognition.
+
+**Note that the competition model in docker container as described in our technical report is slightly different from the recognition model trained from this updated repository.**
 
 ## Dependencies
 
@@ -119,4 +121,3 @@ $(localhost or remote server ip address):5000
 ```
 ![](imgs/web.png)
 
-**Note that the competition model in docker container is slightly different from the recognition model trained from this updated repository.**