modify readme

 # ML base Spacing Correcter
-This model is improved version of [TrainKoSpacing](https://github.com/haven-jeon/TrainKoSpacing "TrainKoSpacing"), using FastText instead of Word2Vec
+본 프로젝트는 (주) 미리내에서 진행한 산학 연계 프로젝트로 별도 사내의 버전 관리 서비스를 이용하였음으로, 히스토리 내역 없이 소스 코드 및 데모 코드만 첨부하였음.
+
+## Introduction
+This model is improved version of [TrainKoSpacing](https://github.com/haven-jeon/TrainKoSpacing "TrainKoSpacing"), using FastText instead of Word2Vec.
+
+If you want detail information you can watch our [presentation video](https://drive.google.com/file/d/1f-D3DC8cnrRniLvoAJ4WyreCjoo316Yc/view?usp=sharing "KCC Presentation Video "presentation video")
+
 
 ## Performances
 | Model  | Test Accuracy(%)   | Encoding Time Cost |
@@ -12,7 +18,9 @@ This model is improved version of [TrainKoSpacing](https://github.com/haven-jeon
 #### Corpus
 
 We mainly focus on the National Institute of Korean Language 모두의 말뭉치 corpus and National Information Society Agency AI-Hub data. However, due to the license issue, we are restricted to distribute this dataset. You should be able to get them throw the link below
+
 [National Institute of Korean Language 모두의 말뭉치](https://corpus.korean.go.kr/).
+
 [National Information Society Agency AI-Hub](https://aihub.or.kr/aihub-data/natural-language/about "National Information Society Agency AI-Hub")
 
 #### Data format
@@ -34,8 +42,11 @@ Bziped file consisting of one sentence per line.
 ### Word Embedding
 #### 자모분해
 To get similar shpae of Korean charector, use 자모분해 FastText word embedding.
+
 ex)
+
 자연어처리
+
 ㅈ ㅏ – ㅇ ㅕ ㄴ ㅇ ㅓ – ㅊ ㅓ – ㄹ ㅣ –
 
 #### 2 stage FastText
@@ -47,6 +58,7 @@ Because middle part of output distribution are evenly distributed.
 ![probability_distribution_of_output_vector](img/probability_distribution_of_output_vector.png)
 
 Use log transform and second derivative
+
 result:
 ![Thresholding_result](img/Thresholding_result.png)
 
@@ -110,7 +122,24 @@ Directory guide for embedding model files
 	- **kospacing_wv.np**
 	- **w2idx.dic**
 
-### Reference
+## Demo
+![demo_img](img/demo_screenshot.png)
+You can watch Demo video [here](https://drive.google.com/file/d/1fYKapmplTmVKVxypj0-bB2TFV_2IiBO_/view?usp=sharing "here")
+
+### How to Run Demo
+#### 1. run demo server
+```bash
+cd demo
+python server.py
+```
+#### 2. open demo client page
+open html file on path: demo/front-client/client_demo.html
+
+Input Korean sentence and click submit
+
+
+## Reference
 TrainKoSpacing: https://github.com/haven-jeon/TrainKoSpacing
+
 딥 러닝을 이용한 자연어 처리 입문: https://wikidocs.net/book/2155