如何写手写文字识别脚本

wen 实用脚本 2026-06-28 253

本文目录导读：

如何写手写文字识别脚本

使用Tesseract OCR（最简单的方案）
使用深度学习模型（高精度方案）
使用EasyOCR
完整的GUI应用
批量处理脚本
图像预处理优化
性能优化建议
选择建议

我来详细介绍如何编写手写文字识别脚本,包括多种实现方案：

使用Tesseract OCR（最简单的方案）

安装准备

# 安装Tesseract
pip install pytesseract pillow
# Windows需要下载安装Tesseract
# https://github.com/UB-Mannheim/tesseract/wiki

基础脚本

import pytesseract
from PIL import Image
import cv2
import numpy as np
def preprocess_image(image_path):
    """图像预处理"""
    # 读取图像
    img = cv2.imread(image_path)
    # 转换为灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 二值化处理
    _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    # 降噪
    denoised = cv2.medianBlur(thresh, 3)
    return denoised
def recognize_handwriting(image_path):
    """识别手写文字"""
    # 预处理图像
    processed_img = preprocess_image(image_path)
    # 配置Tesseract参数
    custom_config = r'--oem 3 --psm 6 -c tessedit_char_whitelist=0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
    # 识别文字
    text = pytesseract.image_to_string(
        processed_img, 
        lang='chi_sim+eng',  # 中文+英文
        config=custom_config
    )
    return text.strip()
# 使用示例
result = recognize_handwriting('handwriting.jpg')
print(f"识别结果: {result}")

使用深度学习模型（高精度方案）

使用PaddleOCR

# 安装PaddleOCR
# pip install paddlepaddle paddleocr
from paddleocr import PaddleOCR
# 初始化OCR
ocr = PaddleOCR(
    use_angle_cls=True,  # 使用方向分类
    lang='ch',           # 中文识别
    use_gpu=False        # 是否使用GPU
)
def recognize_with_paddle(image_path):
    """使用PaddleOCR识别手写文字"""
    result = ocr.ocr(image_path, cls=True)
    text_result = []
    for line in result:
        for word_info in line:
            text = word_info[1][0]  # 文字内容
            confidence = word_info[1][1]  # 置信度
            text_result.append({
                'text': text,
                'confidence': confidence
            })
    return text_result
# 使用示例
results = recognize_with_paddle('handwriting.jpg')
for item in results:
    print(f"文字: {item['text']}, 置信度: {item['confidence']:.2f}")

使用EasyOCR

# pip install easyocr
import easyocr
# 初始化识别器
reader = easyocr.Reader(['ch_sim', 'en'])  # 中英文
def recognize_with_easyocr(image_path):
    """使用EasyOCR识别"""
    results = reader.readtext(image_path)
    text_result = []
    for (bbox, text, confidence) in results:
        text_result.append({
            'text': text,
            'confidence': confidence,
            'bbox': bbox
        })
    return text_result
# 使用示例
results = recognize_with_easyocr('handwriting.jpg')
for item in results:
    print(f"文字: {item['text']}, 置信度: {item['confidence']:.2f}")

完整的GUI应用

import tkinter as tk
from tkinter import filedialog, messagebox
from PIL import Image, ImageTk
import pytesseract
import cv2
import numpy as np
class HandwritingRecognizer:
    def __init__(self, root):
        self.root = root
        self.root.title("手写文字识别工具")
        self.root.geometry("800x600")
        # 创建界面组件
        self.create_widgets()
    def create_widgets(self):
        # 选择图片按钮
        self.btn_select = tk.Button(
            self.root, 
            text="选择图片", 
            command=self.select_image
        )
        self.btn_select.pack(pady=10)
        # 图片显示区域
        self.label_image = tk.Label(self.root)
        self.label_image.pack(pady=10)
        # 识别按钮
        self.btn_recognize = tk.Button(
            self.root, 
            text="识别文字", 
            command=self.recognize_text,
            state=tk.DISABLED
        )
        self.btn_recognize.pack(pady=5)
        # 结果显示
        self.text_result = tk.Text(self.root, height=10, width=60)
        self.text_result.pack(pady=10)
        # 复制按钮
        self.btn_copy = tk.Button(
            self.root, 
            text="复制结果", 
            command=self.copy_result
        )
        self.btn_copy.pack(pady=5)
    def select_image(self):
        file_path = filedialog.askopenfilename(
            filetypes=[("Image files", "*.jpg *.jpeg *.png *.bmp")]
        )
        if file_path:
            self.image_path = file_path
            # 显示图片
            image = Image.open(file_path)
            image.thumbnail((400, 300))
            photo = ImageTk.PhotoImage(image)
            self.label_image.config(image=photo)
            self.label_image.image = photo
            self.btn_recognize.config(state=tk.NORMAL)
    def recognize_text(self):
        if hasattr(self, 'image_path'):
            try:
                # 图像预处理
                img = cv2.imread(self.image_path)
                gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                _, thresh = cv2.threshold(
                    gray, 0, 255, 
                    cv2.THRESH_BINARY + cv2.THRESH_OTSU
                )
                # 识别文字
                text = pytesseract.image_to_string(thresh, lang='chi_sim+eng')
                # 显示结果
                self.text_result.delete(1.0, tk.END)
                self.text_result.insert(1.0, text.strip())
            except Exception as e:
                messagebox.showerror("错误", f"识别失败：{str(e)}")
    def copy_result(self):
        text = self.text_result.get(1.0, tk.END).strip()
        if text:
            self.root.clipboard_clear()
            self.root.clipboard_append(text)
            messagebox.showinfo("成功", "结果已复制到剪贴板")
# 运行应用
if __name__ == "__main__":
    root = tk.Tk()
    app = HandwritingRecognizer(root)
    root.mainloop()

批量处理脚本

import os
from pathlib import Path
import pandas as pd
def batch_recognize(input_folder, output_file):
    """批量识别手写文字"""
    results = []
    # 支持的文件格式
    image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff'}
    # 遍历文件夹
    for image_path in Path(input_folder).iterdir():
        if image_path.suffix.lower() in image_extensions:
            try:
                text = recognize_handwriting(str(image_path))
                results.append({
                    'filename': image_path.name,
                    'text': text,
                    'status': '成功'
                })
                print(f"处理完成: {image_path.name}")
            except Exception as e:
                results.append({
                    'filename': image_path.name,
                    'text': '',
                    'status': f'失败: {str(e)}'
                })
    # 保存到Excel
    df = pd.DataFrame(results)
    df.to_excel(output_file, index=False)
    print(f"结果已保存到: {output_file}")
# 使用示例
batch_recognize('handwriting_images/', 'recognition_results.xlsx')

图像预处理优化

def advanced_preprocess(image_path):
    """高级图像预处理"""
    img = cv2.imread(image_path)
    # 1. 转换为灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 2. 自适应阈值
    binary = cv2.adaptiveThreshold(
        gray, 255, 
        cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
        cv2.THRESH_BINARY, 11, 2
    )
    # 3. 去噪声
    kernel = np.ones((1, 1), np.uint8)
    opening = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
    # 4. 边缘增强
    laplacian = cv2.Laplacian(gray, cv2.CV_64F)
    sharpened = cv2.convertScaleAbs(gray - 0.5 * laplacian)
    # 5. 扭曲校正
    coords = np.column_stack(np.where(binary > 0))
    angle = cv2.minAreaRect(coords)[-1]
    if angle < -45:
        angle = 90 + angle
    (h, w) = img.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, angle, 1.0)
    rotated = cv2.warpAffine(
        gray, M, (w, h), 
        flags=cv2.INTER_CUBIC, 
        borderMode=cv2.BORDER_REPLICATE
    )
    return rotated

性能优化建议

def optimize_recognition(image_path):
    """优化识别流程"""
    # 1. 区域检测（只识别文字区域）
    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 使用MSER检测文字区域
    mser = cv2.MSER_create()
    regions, _ = mser.detectRegions(gray)
    # 合并邻近区域
    hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]
    # 2. 裁剪并识别每个区域
    results = []
    for hull in hulls:
        x, y, w, h = cv2.boundingRect(hull)
        roi = gray[y:y+h, x:x+w]
        # 只识别大于最小尺寸的区域
        if w > 20 and h > 20:
            text = pytesseract.image_to_string(roi, config='--psm 8')
            if text.strip():
                results.append(text.strip())
    return ' '.join(results)