python开发传统蒙古文OCR（一）

传统蒙古文的书写形式是从上到下，从左到有的形式。所以跟其他语言的OCR不一。不过流程都差不多，灰度化，然后查找文字图片，然后进一步识别比对或机器学习，然后识别出相应文字。

1.灰度化

首先打开测试图片。放大两倍，然后二值化（灰度化）。灰度化很简单，代码如下：

import osimport cv2import numpy as npimport matplotlib.pyplot as plt base_dir = "C:/Users/AyonA333/Desktop/"path_test_image = os.path.join(base_dir, "data.png")image_color = cv2.imread(path_test_image)new_shape = (image_color.shape[1], image_color.shape[0])image_color = cv2.resize(image_color, new_shape)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)cv2.imshow('binary image', adaptive_threshold)cv2.waitKey(0)

2.垂直分割

horizontal_sum = np.sum(adaptive_threshold, axis=0)plt.plot(horizontal_sum, range(horizontal_sum.shape[0]))plt.gca().invert_yaxis()plt.show()

二值化以后图片成为一个矩阵，所以每列相加以后可以大概的能知道每列的具体范围，方便于切割每列文字。python的num的有关矩阵的相加如下：

计算出来的值用matplotlib显示出来后：

3.垂直每列文字切割

def extract_peek_ranges_from_array(array_vals, minimun_val=1000, minimun_range=2):    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:            end_i = i            if end_i - start_i >= minimun_range:                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_rangespeek_ranges = extract_peek_ranges_from_array(horizontal_sum)line_seg_adaptive_threshold = np.copy(adaptive_threshold)for i, peek_range in enumerate(peek_ranges):    x = peek_range[0]    y = 0    w = peek_range[1]    h = line_seg_adaptive_threshold.shape[0]    pt1 = (x, y)    pt2 = (x + w, y + h)    cv2.rectangle(line_seg_adaptive_threshold, pt1, pt2, 255)cv2.imshow('line image', line_seg_adaptive_threshold)cv2.waitKey(0)vertical_peek_ranges2d = []for peek_range in peek_ranges:start_x = 0end_x = line_seg_adaptive_threshold.shape[0]line_img = adaptive_threshold[start_x:end_x,peek_range[0]:peek_range[1]]#cv2.imshow('binary image', line_img)vertical_sum = np.sum(line_img, axis=1)#plt.plot(vertical_sum, range(vertical_sum.shape[0]))#plt.gca().invert_yaxis()#plt.show()vertical_peek_ranges = extract_peek_ranges_from_array(vertical_sum,minimun_val=2,minimun_range=1)vertical_peek_ranges2d.append(vertical_peek_ranges)print(vertical_peek_ranges2d)

显示切割出来的一列如下：

4.切割每个列的文字

显示一个列的矩阵值如上，按矩阵的横向最小值来分割出每列的文字。

cnt = 1color = (0, 0, 255)for i, peek_range in enumerate(peek_ranges):    for vertical_range in vertical_peek_ranges2d[i]:        x = peek_range[0]        y = vertical_range[0]        w = peek_range[1] - x        h = vertical_range[1] - y        #print(str(x)+'-'+str(y)+'-'+str(w)+'-'+str(h))        patch = adaptive_threshold[y:y+h,x:x+w]        cv2.imwrite('C:/Users/AyonA333/Desktop/testpic/'+'_%d' %cnt+'.jpg', patch)        cnt += 1        pt1 = (x, y)        pt2 = (x + w, y + h)        cv2.rectangle(image_color, pt1, pt2, color)cv2.imshow('char image', image_color)cv2.waitKey(0)

保存到testpic文件夹里。

这样第二部可以做深度学习训练了。

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