Python-Pytesseract从图像中提取不正确的文本 [英] Python - Pytesseract extracts incorrect text from image

问题描述

我在Python中使用以下代码从图像中提取文本，

I used the below code in Python to extract text from image,

import cv2
import numpy as np
import pytesseract
from PIL import Image

# Path of working folder on Disk
src_path = "<dir path>"

def get_string(img_path):
    # Read image with opencv
    img = cv2.imread(img_path)

    # Convert to gray
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Apply dilation and erosion to remove some noise
    kernel = np.ones((1, 1), np.uint8)
    img = cv2.dilate(img, kernel, iterations=1)
    img = cv2.erode(img, kernel, iterations=1)

    # Write image after removed noise
    cv2.imwrite(src_path + "removed_noise.png", img)

    #  Apply threshold to get image with only black and white
    #img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)

    # Write the image after apply opencv to do some ...

    cv2.imwrite(src_path + "thres.png", img)

    # Recognize text with tesseract for python
    result = pytesseract.image_to_string(Image.open(img_path))#src_path+ "thres.png"))

    # Remove template file
    #os.remove(temp)

    return result


print '--- Start recognize text from image ---'
print get_string(src_path + "test.jpg")

print "------ Done -------"

但是输出不正确.输入文件是

But the output is incorrect.. The input file is,

收到的输出是"0001"，而不是"D001"

The output received is '0001' instead of 'D001'

收到的输出是'3001'而不是'B001'

The output received is '3001' instead of 'B001'

从图像中检索正确的字符，以及训练pytesseract返回图像中所有字体类型的正确字符(包括粗体字符)所需的代码更改是什么

What is the required code changes to retrieve the right Characters from image, also to train the pytesseract to return the right characters for all font types in image[including Bold characters]

推荐答案

@Maaaaa指出了Tessearact识别文本错误的确切原因.

@Maaaaa has pointed out the exact reason for incorrect text recognition by Tessearact.

但是仍然可以通过在tesseract输出上应用一些后处理步骤来改善最终输出.如果有帮助，您可以考虑并使用以下几点:

But still you can improve your final output by applying some post processing steps on the tesseract output. Here are a few points that you can think about and use them if it helps:

1. 尝试在Tesseract输入参数中禁用字典检查功能.
2. 使用数据集中基于启发式的信息.从给定的示例图像中，我猜每个单词/序列的第一个字符是一个字母，因此您可以根据数据集用最可能的字母替换输出中的第一个数字， 例如，"0"可以替换为D，因此"0001"->"D001"，对于其他情况也是如此.
3. Tesseract还提供了字符级别识别置信度值，因此请使用该信息用具有最高置信度值的字符替换字符.

1. Try disabling the dictionary check feature in Tesseract input parameters.
2. Use heuristic based information from your dataset. From the given sample images in question, i guess first character of each word/sequence is an alphabet so you can replace first digit in your output with most probable alphabet based on your dataset, for example '0' can be replaced with D so '0001' -> 'D001', similarly for other cases too.
3. Tesseract also provides the character level recognition confidence value, so use that information to replace the characters with the one having highest confidence value.

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