Scene Text Recognition Recommendations

Everythin about Scene Text Recognition

SOTA • Papers • Datasets • Code

Contents

• 1. Papers
• 2. Datasets
  • 2.1 Synthetic Datasets
  • 2.2 Benchmarks
• 3. Public Code
  • 3.1 Frameworks
  • 3.2 Algorithms
• 4.SOTA

1.Papers

All Papers Can be Find Here

• Latest Papers:

2.Datasets

2.1 Synthetic Datasets

Dataset	Description	Examples	BaiduNetdisk link
SynthText	9 million synthetic text instance images from a set of 90k common English words. Words are rendered onto nartural images with random transformations		Scene text datasets(提取码:emco)
MJSynth	6 million synthetic text instances. It's a generation of SynthText.		Scene text datasets(提取码:emco)

2.2 Benchmarks

Dataset	Description	Examples	BaiduNetdisk link
IIIT5k-Words(IIIT5K)	3000 test images instances. Take from street scenes and from originally-digital images		Scene text datasets(提取码:emco)
Street View Text(SVT)	647 test images instances. Some images are severely corrupted by noise, blur, and low resolution		Scene text datasets(提取码:emco)
StreetViewText-Perspective(SVT-P)	639 test images instances. It is specifically designed to evaluate perspective distorted textrecognition. It is built based on the original SVT dataset by selecting the images at the sameaddress on Google Street View but with different view angles. Therefore, most text instancesare heavily distorted by the non-frontal view angle.		Scene text datasets(提取码:emco)
ICDAR 2003(IC03)	867 test image instances		Scene text datasets(提取码:mfir)
ICDAR 2013(IC13)	1015 test images instances		Scene text datasets(提取码:emco)
ICDAR 2015(IC15)	2077 test images instances. As text images were taken by Google Glasses without ensuringthe image quality, most of the text is very small, blurred, and multi-oriented		Scene text datasets(提取码:emco)
CUTE80(CUTE)	288 It focuses on curved text recognition. Most images in CUTE have acomplex background, perspective distortion, and poor resolution		Scene text datasets(提取码:emco)

3.1 Public Code

3.1. Frameworks

PaddleOCR (百度)

• PaddlePaddle/PaddleOCR
• 特性 (截取至PaddleOCR)：
  • 使用百度自研深度学习框架PaddlePaddle搭建
  • PP-OCR系列高质量预训练模型，准确的识别效果
    • 超轻量PP-OCRv2系列：检测（3.1M）+ 方向分类器（1.4M）+ 识别（8.5M）= 13.0M
    • 超轻量PP-OCR mobile移动端系列：检测（3.0M）+方向分类器（1.4M）+ 识别（5.0M）= 9.4M
    • 通用PPOCR server系列：检测（47.1M）+方向分类器（1.4M）+ 识别（94.9M）= 143.4M
    • 支持中英文数字组合识别、竖排文本识别、长文本识别
    • 支持多语言识别：韩语、日语、德语、法语
    • 丰富易用的OCR相关工具组件
  • 半自动数据标注工具PPOCRLabel：支持快速高效的数据标注
    • 数据合成工具Style-Text：批量合成大量与目标场景类似的图像
    • 文档分析能力PP-Structure：版面分析与表格识别
    • 支持用户自定义训练，提供丰富的预测推理部署方案
    • 支持PIP快速安装使用
    • 可运行于Linux、Windows、MacOS等多种系统
• 支持算法(识别):
  • CRNN
  • Rosetta
  • STAR-Net
  • RARE
  • SRN
  • NRTR

MMOCR (商汤)

• open-mmlab/mmocr
• 特性(截取至MMOCR):
  • MMOCR 是基于 PyTorch 和 mmdetection 的开源工具箱，专注于文本检测，文本识别以及相应的下游任务，如关键信息提取。 它是 OpenMMLab 项目的一部分。
  • 该工具箱不仅支持文本检测和文本识别，还支持其下游任务，例如关键信息提取。
• 支持算法(识别)
  • CRNN (TPAMI'2016)
  • NRTR (ICDAR'2019)
  • RobustScanner (ECCV'2020)
  • SAR (AAAI'2019)
  • SATRN (CVPR'2020 Workshop on Text and Documents in the Deep Learning Era)
  • SegOCR (Manuscript'2021)

Deep Text Recognition Benchmark (ClovaAI)

• clovaai/deep-text-recognition-benchmark
• 特性:
  • Offical Pytorch implementation of What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
  • 可自定义四阶段组件，如CRNN，ASTER
  • 容易上手，推荐使用

3.2. Algorithms

CRNN

• Lua, Offical, 1.9k ⭐ : bgshih/crnn
  • 官方实现版本，使用Lua
• Pytorch, 1.9k ⭐ : meijeru/crnn.pytorch
  • 推荐使用 🀄
• Tensorflow, 972 ⭐ :MaybeShewill-CV/CRNN_Tensorflow
• Pytorch, 1.4k ⭐ :Sierkinhance/CRNN_Chinese_Characters_Rec
  • 用于中文识别版本的CRNN

ASTER

• Tensorflow, official, 651 ⭐ : bgshih/aster
  • 官方实现版本，使用Tensorflow
• Pytorch, 535 ⭐ :ayumuymk/aster.pytorch
  • Pytorch版本，准确率相较原文有明显提升

MORANv2

• Pytorch, official, 572 ⭐ :Canjie-Luo/MORAN_v2
  • MORAN v2版本。更加稳定的单阶段训练，更换ResNet做backbone，使用双向解码器

4.SOTA

Baek's Reimplementation Version