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+# Easy Yolo OCR
+
+![issue badge](https://img.shields.io/github/license/aqntks/recog)
+![issue badge](https://img.shields.io/badge/build-passing-brightgreen)
+![issue badge](https://img.shields.io/badge/%EB%8B%A4%EA%B5%AD%EC%96%B4-%EC%A7%80%EC%9B%90-yellow)
+[![LinkedIn Badge](http://img.shields.io/badge/LinkedIn-@InpyoHong-0072b1?style=flat&logo=linkedin&link=https://www.linkedin.com/in/inpyo-hong-886781212/)](https://www.linkedin.com/in/inpyo-hong-886781212/)
+
+Proceed with text detection only in the desired area
+
+This repository is a project using [yolov5](https://github.com/ultralytics/yolov5) and [EasyOCR](https://github.com/JaidedAI/EasyOCR).
+
+## Introduction
+
+The existing OCR (Optical Character Recognition) process detects a character area with the Text Detection model and then recognizes the character through the Text Recognition model. These OCR models are effective in recognizing the entire character within a desired document or image.    
+
+However, if you want to detect only the characters in a specific area within an image or document, it detects unnecessary areas too, so the detection speed takes a long time and it is inconvenient to process the result value.
+
+We suggest Easy Yolo OCR for those who want to detect only characters located in a specific pattern or area in various images.
+
+Easy Yolo OCR has changed the Text Detection model for detecting text areas to the Object Detection model used for object detection. Learn a custom detection model that suits you and detect only the desired area of the desired format.
+
+The object detection model uses [yolov5](https://github.com/ultralytics/yolov5), which is actively used in the real-time object detection field. The OCR process benchmarked [EasyOCR](https://github.com/JaidedAI/EasyOCR) and the Text Recognition model was trained through [deep-text-recognition-benchmark](https://github.com/clovaai/deep-text-recognition-benchmark) of Clova AI Research.
+
+- Existing OCR process
+
+![](res/original.jpg)
+
+- Easy Yolo OCR process
+
+![](res/easyyoloocr.jpg)
+
+
+## Installation
+
+
+``` bash
+$ git clone https://github.com/aqntks/Easy-Yolo-OCR
+$ cd Easy-Yolo-OCR
+$ pip install -r requirements.txt
+```
+
+## Prepare Training Data
+### 1. Data location
+Place the image file(jpg, png ... etc) and labeling file(txt) in the "./dataset/custom_data" folder.
+
+---dataset\
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ㄴ---custom_data\
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ㄴ---image1.jpg\
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;---image1.txt\
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;---image2.jpg\
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;---image2.txt\
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;---image3.jpg\
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;---image3.txt
+
+### 2. Configure labeling text(image.txt)
+(ClassIndex)&nbsp;&nbsp;(BoxStartX[value 0-1])&nbsp;&nbsp;(BoxStartY[0-1값])&nbsp;&nbsp;(BoxWidth[0-1값])&nbsp;&nbsp;(BoxHeight[0-1값])\
+(ClassIndex)&nbsp;&nbsp;(BoxStartX[value 0-1])&nbsp;&nbsp;(BoxStartY[0-1값])&nbsp;&nbsp;(BoxWidth[0-1값])&nbsp;&nbsp;(BoxHeight[0-1값])\
+(ClassIndex)&nbsp;&nbsp;(BoxStartX[value 0-1])&nbsp;&nbsp;(BoxStartY[0-1값])&nbsp;&nbsp;(BoxWidth[0-1값])&nbsp;&nbsp;(BoxHeight[0-1값])\
+
+```bash
+# ex) image1.txt 
+
+0 0.6659722222222223 0.11302083333333333 0.4013888888888889 0.06770833333333333
+9 0.48333333333333334 0.12552083333333333 0.025 0.036458333333333336
+3 0.5145833333333334 0.1265625 0.02638888888888889 0.036458333333333336
+1 0.5479166666666667 0.125 0.0375 0.0375
+4 0.5798611111111112 0.125 0.029166666666666667 0.03333333333333333
+8 0.6145833333333334 0.12447916666666667 0.03194444444444445 0.03854166666666667
+4 0.6479166666666667 0.12395833333333334 0.03194444444444445 0.041666666666666664
+2 0.68125 0.12447916666666667 0.03194444444444445 0.03229166666666666
+3 0.7145833333333333 0.12395833333333334 0.03194444444444445 0.03125
+7 0.7465277777777778 0.12552083333333333 0.029166666666666667 0.034375
+0 0.78125 0.12239583333333333 0.03194444444444445 0.03229166666666666
+8 0.8104166666666667 0.125 0.029166666666666667 0.0375
+0 0.8423611111111111 0.12343749999999999 0.034722222222222224 0.036458333333333336
+```
+
+### 3. Create train, valid, test file
+./dataset/custom_train.txt\
+./dataset/custom_valid.txt\
+./dataset/custom_train_test.txt
+
+```bash
+# ex) custom_train.txt
+
+./dataset/custom_data/image001.jpg
+./dataset/custom_data/image002.jpg
+./dataset/custom_data/image003.jpg
+./dataset/custom_data/image004.jpg
+              .
+              .
+              .
+```
+```bash
+# ex) custom_valid.txt
+
+./dataset/custom_data/image101.jpg
+./dataset/custom_data/image102.jpg
+./dataset/custom_data/image103.jpg
+./dataset/custom_data/image104.jpg
+              .
+              .
+              .
+```
+```bash
+# ex) custom_test.txt (optional)
+
+./dataset/custom_data/image151.jpg
+./dataset/custom_data/image152.jpg
+./dataset/custom_data/image153.jpg
+./dataset/custom_data/image154.jpg
+              .
+              .
+              .
+```
+
+### 4. Write custom.yaml
+Create the data/custom.yaml file and write the following
+
+```bash
+# custom.yaml
+
+path: ./dataset/custom_data
+train: ./dataset/custom_train.txt
+val:  ./dataset/custom_valid.txt
+test:  ./dataset/custom_train_test.txt  # (optional)
+
+nc: 10  # number of classes
+names: ['title', 'name', 'personal_id', 'text_box_1', 'text_box_2', 'price', 'address', 'age', 'date', 'count']  # class names
+```
+
+## Train Detection Model
+``` bash
+$ cd yolov5
+```
+
+```bash
+$ python train.py --data data/custom.yaml --weights 'yolov5s.pt' --img 640 --batch-size 64 --epochs 300
+                                                     yolov5m.pt        960              40          100
+                                                     yolov5l.pt        480              24           50 
+                                                     yolov5x.pt        320              16           30 
+```
+
+## Setting Config
+```bash
+# config.yaml
+
+images: image                                # detection image folder
+
+detection: weights/example.pt                # trained detecting model
+detection-size: 640                          # Detection image size
+detection-confidence: 0.25                   # detecting confidence
+detection-iou: 0.45                          # detecting iou
+```
+
+
+## OCR
+
+```bash
+$ python main.py --gpu 0 --lang en ko
+$ python main.py --gpu 0 --lang en
+$ python main.py --gpu -1 --lang ko         # --gpu -1 : cpu mode
+```
+