OCR识别部分数据集中字体和背景是比较重要的方面，在实际的场景下收集不到那么多真实的样本，在训练开始的时候手工构造一批训练数据还是很有必要的。可以用这一批数据预训练后面用真实数据来微调，也可以直接把这些数据和真实数据混合在一起训练。
在构造数据集的方面TextRecognitionDataGenerator还是比较好用了，这是按行生成文本的,可以生成bounding boxes，和mask。

1. 开源项目TextRecognitionDataGenerator

链接TextRecognitionDataGenerator

支持的一些有用的操作

• blur 模糊操作
• 在背景图上写字（这个很有用，选择一些真实场景的背景图，然后把文字写在上面，看起来更真实一些）
• 角度旋转
• 生成boundingbox和mask标签
• 字间距调整
• 字体颜色设置
• 拉丁语系的提供了100多种字体
• …

安装有两种方式，

• pip 安装

pip install trdg
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• 源码安装
  下载源码，cd进入setup.py所在目录，然后安装依赖项

pip install -r requirements.txt
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使用的时候用trdg文件夹下的run.py文件生成数据，默认在TextRecognitionDataGenerator-master\trdg路径下

2. 使用

常用选项

python run.py 
--font_dir fonts\latin 	#字体文件，可以选择文件夹或者单个字体库
--dict dicts\myDict.txt 	#字典路径
-c 50 		#一共生成多少个图片
--output_dir outputs #保存路径
-k 5 -rk  		#-k 5表示旋转的角度为5°，后面接-rk表示在-5，+5范围内随机，
-bl 3 -rbl		#-bl 表示blur 高斯模糊，后面接半径， -rbl表示高斯核的半径在0-3之间
--case upper 	#upper表示使用大写字符，lower表示用小写
-b 3 -id images	#-b表示背景，3表示用图片做背景，-id：image_dir,指定背景图片路径
-f 64		# --format ,生成的图片的高度
-tc #22211f	#--text_color 6位的16进制数，RGB格式直接翻译，例如：rgb=[10,15,255]==>#0a 0e ff 
-obb 1		#生成bonding box，
		#格式是嵌套的，第一行：4个数，分别为第一个字符左上角x,y 最后一个字符的右下角的x，y
		#第二行：4个数，分别为第二个字符左上角的x,y，最后一个字符的右下角的x，y
		#第n行：4个数，分别为第n个字符左上角的x,y，最后一个字符的右下角的x，y

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例如：

python run.py --font_dir fonts\latin --dict dicts\OCRDict.txt -c 52 --output_dir K:\imageData\OCR\ocr_dataset\test  -k 6 -rk -bl 1 -rbl -b 3 -id K:\imageData\OCR\ocr_background\yellow -f 80
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查看使用帮助就能只知道完整的用法了

python run.py --help
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usage: run.py [-h] [--output_dir [OUTPUT_DIR]] [-i [INPUT_FILE]]
              [-l [LANGUAGE]] -c [COUNT] [-rs] [-let] [-num] [-sym]
              [-w [LENGTH]] [-r] [-f [FORMAT]] [-t [THREAD_COUNT]]
              [-e [EXTENSION]] [-k [SKEW_ANGLE]] [-rk] [-wk] [-bl [BLUR]]
              [-rbl] [-b [BACKGROUND]] [-hw] [-na NAME_FORMAT]
              [-om OUTPUT_MASK] [-obb OUTPUT_BBOXES] [-d [DISTORSION]]
              [-do [DISTORSION_ORIENTATION]] [-wd [WIDTH]] [-al [ALIGNMENT]]
              [-or [ORIENTATION]] [-tc [TEXT_COLOR]] [-sw [SPACE_WIDTH]]
              [-cs [CHARACTER_SPACING]] [-m [MARGINS]] [-fi] [-ft [FONT]]
              [-fd [FONT_DIR]] [-id [IMAGE_DIR]] [-ca [CASE]] [-dt [DICT]]
              [-ws] [-stw [STROKE_WIDTH]] [-stf [STROKE_FILL]]
              [-im [IMAGE_MODE]]

Generate synthetic text data for text recognition.

optional arguments:
  -h, --help            show this help message and exit
  --output_dir [OUTPUT_DIR]
                        The output directory
  -i [INPUT_FILE], --input_file [INPUT_FILE]
                        When set, this argument uses a specified text file as
                        source for the text
  -l [LANGUAGE], --language [LANGUAGE]
                        The language to use, should be fr (French), en
                        (English), es (Spanish), de (German), ar (Arabic), cn
                        (Chinese), ja (Japanese) or hi (Hindi)
  -c [COUNT], --count [COUNT]
                        The number of images to be created.
  -rs, --random_sequences
                        Use random sequences as the source text for the
                        generation. Set '-let','-num','-sym' to use
                        letters/numbers/symbols. If none specified, using all
                        three.
  -let, --include_letters
                        Define if random sequences should contain letters.
                        Only works with -rs
  -num, --include_numbers
                        Define if random sequences should contain numbers.
                        Only works with -rs
  -sym, --include_symbols
                        Define if random sequences should contain symbols.
                        Only works with -rs
  -w [LENGTH], --length [LENGTH]
                        Define how many words should be included in each
                        generated sample. If the text source is Wikipedia,
                        this is the MINIMUM length
  -r, --random          Define if the produced string will have variable word
                        count (with --length being the maximum)
  -f [FORMAT], --format [FORMAT]
                        Define the height of the produced images if
                        horizontal, else the width
  -t [THREAD_COUNT], --thread_count [THREAD_COUNT]
                        Define the number of thread to use for image
                        generation
  -e [EXTENSION], --extension [EXTENSION]
                        Define the extension to save the image with
  -k [SKEW_ANGLE], --skew_angle [SKEW_ANGLE]
                        Define skewing angle of the generated text. In
                        positive degrees
  -rk, --random_skew    When set, the skew angle will be randomized between
                        the value set with -k and it's opposite
  -wk, --use_wikipedia  Use Wikipedia as the source text for the generation,
                        using this paremeter ignores -r, -n, -s
  -bl [BLUR], --blur [BLUR]
                        Apply gaussian blur to the resulting sample. Should be
                        an integer defining the blur radius
  -rbl, --random_blur   When set, the blur radius will be randomized between 0
                        and -bl.
  -b [BACKGROUND], --background [BACKGROUND]
                        Define what kind of background to use. 0: Gaussian
                        Noise, 1: Plain white, 2: Quasicrystal, 3: Image
  -hw, --handwritten    Define if the data will be "handwritten" by an RNN
  -na NAME_FORMAT, --name_format NAME_FORMAT
                        Define how the produced files will be named. 0:
                        [TEXT]_[ID].[EXT], 1: [ID]_[TEXT].[EXT] 2: [ID].[EXT]
                        + one file labels.txt containing id-to-label mappings
  -om OUTPUT_MASK, --output_mask OUTPUT_MASK
                        Define if the generator will return masks for the text
  -obb OUTPUT_BBOXES, --output_bboxes OUTPUT_BBOXES
                        Define if the generator will return bounding boxes for
                        the text, 1: Bounding box file, 2: Tesseract format
  -d [DISTORSION], --distorsion [DISTORSION]
                        Define a distorsion applied to the resulting image. 0:
                        None (Default), 1: Sine wave, 2: Cosine wave, 3:
                        Random
  -do [DISTORSION_ORIENTATION], --distorsion_orientation [DISTORSION_ORIENTATION]
                        Define the distorsion's orientation. Only used if -d
                        is specified. 0: Vertical (Up and down), 1: Horizontal
                        (Left and Right), 2: Both
  -wd [WIDTH], --width [WIDTH]
                        Define the width of the resulting image. If not set it
                        will be the width of the text + 10. If the width of
                        the generated text is bigger that number will be used
  -al [ALIGNMENT], --alignment [ALIGNMENT]
                        Define the alignment of the text in the image. Only
                        used if the width parameter is set. 0: left, 1:
                        center, 2: right
  -or [ORIENTATION], --orientation [ORIENTATION]
                        Define the orientation of the text. 0: Horizontal, 1:
                        Vertical
  -tc [TEXT_COLOR], --text_color [TEXT_COLOR]
                        Define the text's color, should be either a single hex
                        color or a range in the ?,? format.
  -sw [SPACE_WIDTH], --space_width [SPACE_WIDTH]
                        Define the width of the spaces between words. 2.0
                        means twice the normal space width
  -cs [CHARACTER_SPACING], --character_spacing [CHARACTER_SPACING]
                        Define the width of the spaces between characters. 2
                        means two pixels
  -m [MARGINS], --margins [MARGINS]
                        Define the margins around the text when rendered. In
                        pixels
  -fi, --fit            Apply a tight crop around the rendered text
  -ft [FONT], --font [FONT]
                        Define font to be used
  -fd [FONT_DIR], --font_dir [FONT_DIR]
                        Define a font directory to be used
  -id [IMAGE_DIR], --image_dir [IMAGE_DIR]
                        Define an image directory to use when background is
                        set to image
  -ca [CASE], --case [CASE]
                        Generate upper or lowercase only. arguments: upper or
                        lower. Example: --case upper
  -dt [DICT], --dict [DICT]
                        Define the dictionary to be used
  -ws, --word_split     Split on words instead of on characters (preserves
                        ligatures, no character spacing)
  -stw [STROKE_WIDTH], --stroke_width [STROKE_WIDTH]
                        Define the width of the strokes
  -stf [STROKE_FILL], --stroke_fill [STROKE_FILL]
                        Define the color of the contour of the strokes, if
                        stroke_width is bigger than 0
  -im [IMAGE_MODE], --image_mode [IMAGE_MODE]
                        Define the image mode to be used. RGB is default, L
                        means 8-bit grayscale images, 1 means 1-bit binary
                        images stored with one pixel per byte, etc.
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3. 示例

• 下面这几张图是原项目中的示例图

• 下面的图片使用的是真实场景下的一些背景图，按字典把字符写在上面