---
# System prepended metadata

title: 討論 Week18 的主題
tags: [OCR track]

---

# 討論 Week18 的主題
###### tags: `OCR track`
1. 反光偵測模型實作
2. text detection 新演算法實作
3. 訓練新的 OCR model
4. hand-writing OCR model
5. 分類模型
6. 其他想法

## RPA support CV 任務

* 昊中：CRNN + CTC (Pytorch)(用cc_ws_ocr專案的dataset去訓練)
* 信賢：data augmentation, OCR, yolo backbone (3 選 1)
* 沛筠：data augmentation
* RPA 任務：驗證碼 OCR
## OCR 步驟

* 分類模型
    * efficientNet
    * mobilenet
* 影像前處理
    * 去噪
    * data augmentation
* detection
    * text (e.g., craft)
    * object (e.g., yolov4)
    * segmentation
* OCR
    * 前處理：e.g. 文字加深加黑再套用 OCR model
    * 精進舊版本的 recogntion model
    * hand-writing OCR model
        * 信用卡自扣申請書
            * 排除
                * crnn+ctc: 90 ~ 91%
                * [clova](https://github.com/clovaai/deep-text-recognition-benchmark)
                * attention
        * 支票手寫
        * 匯款單
## 執行方式

* :star: 報告順序:
    * week 18: 固定演算法，嘗試多種 data augmentation 對 OCR performance 的貢獻 (固定 CRNN+CTC, 參考 if_cv_dev)
    * week 21: 先找幾個OCR演算法(except CRNN+CTC、attention、clova)，使用相同的資料去比較 performance
* 後續比較方式:
    * case 1: CRNN + CTC & 原始資料 (baseline)
    * case 2: hand-writing OCR model & 原始資料
    * case 3: CRNN + CTC & data augmentation (method 1, 2, ...)
    * case 4: hand-writing OCR model & data augmentation


### augmentation 筆記

## 方法論

信賢：
* :star:Image-to-Image Translation with Conditional Adversarial Networks
  - [blog](https://kaonashi-tyc.github.io/2017/04/06/zi2zi.html)
  - [paper](https://arxiv.org/pdf/1611.07004.pdf)
  - [code1](https://github.com/kaonashi-tyc/zi2zi)
  - [code2](https://github.com/jasonlo0509/Font2Font)
* TextRecognitionDataGenerator
  [code](https://github.com/Belval/TextRecognitionDataGenerator)
* Text Renderer
  [code](https://github.com/Sanster/text_renderer)
* Augmentor
  [code](https://github.com/mdbloice/Augmentor)

昱睿：
* :star:AutoAugment: Learning Augmentation Strategies from Data
    * paper: [AutoAugment: Learning Augmentation Strategies from Data](https://arxiv.org/pdf/1805.09501.pdf)
    * code: [DeepVoltaire/AutoAugment](https://github.com/DeepVoltaire/AutoAugment)

立晟：
* :star:imgaug
  - [docs](https://imgaug.readthedocs.io/en/latest/source/examples_basics.html)
* :star:Albumentations
  - [paper](https://arxiv.org/pdf/1809.06839.pdf)
  - [docs](https://albumentations.readthedocs.io/en/latest/)


昊中：
* imgaug
* https://github.com/Belval/TextRecognitionDataGenerator
* https://github.com/Sanster/text_renderer
* https://github.com/williamyang1991/TET-GAN
* https://github.com/pyliaorachel/cross-lingual-font-style-transfer
* (備)https://github.com/Canjie-Luo/Text-Image-Augmentation
* :star:https://github.com/RubanSeven/Text-Image-Augmentation-python

沛筠：
* :star:RandAugment: Practical automated data augmentation with a reduced search space
    * paper: [RandAugment: Practical automated data augmentation with a reduced search space](https://arxiv.org/pdf/1909.13719.pdf)
    * code: [pytorch-randaugment](https://github.com/ildoonet/pytorch-randaugment)
    * code: [imgaug (現成)](https://github.com/aleju/imgaug/blob/master/imgaug/augmenters/collections.py)
        * 為 pytorch 版本的延伸
    * code: [tensorflow (較舊)](https://github.com/tensorflow/tpu/blob/1ebfc4b5fa6b5fad0bd7d422832ae8826e8059f2/models/official/efficientnet/autoaugment.py)