图片文字分割

图片文字分割

基于像素识别图片文字

目前已经有成熟的OCR图片文字识别技术了,但还是学习一下一些基本的实现知识。

OCR

OCR(optical character recognition)文字识别是指电子设备(例如扫描仪或数码相机)检查纸上打印的字符,然后用字符识别方法将形状翻译成计算机文字的过程;即,对文本资料进行扫描,然后对图像文件进行分析处理,获取文字及版面信息的过程。如何除错或利用辅助信息提高识别正确率,是OCR最重要的课题的友好性,产品的稳定性,易用性及可行性等。

OCR技术目前用于各大领域用于文字识别,那么从拍的一张图片如何通过分割得到其中的字呢,下面用python语言进行对图片的分割:

需要用到的包:

import os
import cv2
import numpy as np

OS模块是一个Python的系统编程的操作模块,可以处理文件和目录。

CV2是OpenCV官方的一个扩展库,含有各种有关图像处理的的函数以及进程。

NumPy是Python的一种开源的数值计算扩展。这种工具可用来存储和处理大型矩阵

读取图片:

image = cv2.imread("origin.png")

二值化

# adaptiveThreshold参数解释
# 第一个原始图像
# 第二个像素值上限
# 第三个cv2.ADAPTIVE_THRESH_GAUSSIAN_C :领域内像素点加权和,权重为一个高斯窗口
# 第四个值的赋值方法:只有cv2.THRESH_BINARY 和cv2.THRESH_BINARY_INV
# 第五个Block size:规定领域大小(一个正方形的领域)
# 第六个常数C,阈值等于均值或者加权值减去这个常数(为0相当于阈值 就是求得领域内均值或者加权值)
image = cv2.cvtColor(image_color, cv2.COLOR_BGR2GRAY)
adaptive_threshold = cv2.adaptiveThreshold(image,255,\
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
cv2.THRESH_BINARY_INV, 11, 2)

二值化后方便处理图片像素灰度值

原图
图片文字分割_第1张图片

反相处理后
图片文字分割_第2张图片

根据垂直和水平方向像素与空间阈值确定方格:
分割后:
图片文字分割_第3张图片
提取每个字:
图片文字分割_第4张图片
完整代码

import os
import cv2
import numpy as np

base_dir = "./origin/"
dst_dir = "./result/"
min_val = 10
min_range = 30

count = 0
def extract_peek(array_vals, minimun_val, minimun_range):
    start_i = None
    end_i = None
    peek_ranges = []
    for i, val in enumerate(array_vals):
        if val > minimun_val and start_i is None:
            start_i = i
        elif val > minimun_val and start_i is not None:
            pass
        elif val < minimun_val and start_i is not None:
            if i - start_i >= minimun_range:
                end_i = i
                print(end_i - start_i)
                peek_ranges.append((start_i, end_i))
                start_i = None
                end_i = None
        elif val < minimun_val and start_i is None:
            pass
        else:
            raise ValueError("cannot parse this case...")
    return peek_ranges
def cutImage(img, peek_range):
    global count
    for i, peek_range in enumerate(peek_ranges):
        for vertical_range in vertical_peek_ranges2d[i]:
            x = vertical_range[0]
            y = peek_range[0]
            w = vertical_range[1] - x
            h = peek_range[1] - y
            pt1 = (x, y)
            pt2 = (x + w, y + h)
            count += 1
            img1 = img[y:peek_range[1], x:vertical_range[1]]
            new_shape = (150, 150)
            img1 = cv2.resize(img1, new_shape)
            cv2.imwrite(dst_dir + str(count) + ".png", img1)
            # cv2.rectangle(img, pt1, pt2, color)
for fileName in os.listdir(base_dir):
    img = cv2.imread(base_dir + fileName)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    adaptive_threshold = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \
                                               cv2.THRESH_BINARY_INV, 11, 2)
    horizontal_sum = np.sum(adaptive_threshold, axis=1)
    peek_ranges = extract_peek(horizontal_sum, min_val, min_range)
    line_seg_adaptive_threshold = np.copy(adaptive_threshold)
    for i, peek_range in enumerate(peek_ranges):
        x = 0
        y = peek_range[0]
        w = line_seg_adaptive_threshold.shape[1]
        h = peek_range[1] - y
        pt1 = (x, y)
        pt2 = (x + w, y + h)
        cv2.rectangle(line_seg_adaptive_threshold, pt1, pt2, 255)
    vertical_peek_ranges2d = []
    for peek_range in peek_ranges:
        start_y = peek_range[0]
        end_y = peek_range[1]
        line_img = adaptive_threshold[start_y:end_y, :]
        vertical_sum = np.sum(line_img, axis=0)
        vertical_peek_ranges = extract_peek(
            vertical_sum, min_val, min_range)
        vertical_peek_ranges2d.append(vertical_peek_ranges)
    cutImage(img, peek_range)

关于提取如下无规律的手写字可以参考基于轮廓提取图像文字
图片文字分割_第5张图片

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