CNN手写汉字识别

最新推荐文章于 2025-06-24 19:27:14 发布

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最新推荐文章于 2025-06-24 19:27:14 发布 · 4.2k 阅读

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#手写汉字识别 #HWDB

该博客介绍了一种基于CNN的手写汉字识别方法。首先，详细阐述了如何将HWDB数据集从gnt格式转换为png格式。接着，提到了参考的GitHub源码链接，该源码适用于在tensorflow1.3环境下训练和测试模型。最后，提供了在ubuntu16.04系统上设置环境和运行模型的步骤，包括选择合适的CUDA版本。

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一、数据集HWDB

由于HWDB数据集解压之后是gnt格式的，需要将其转换为png格式。

https://blog.youkuaiyun.com/yql_617540298/article/details/82740382博客介绍了如何转换。

https://pan.baidu.com/s/1o84jIrg#list/path=%2F百度云中可以直接下载转换后的结果。

二、源码

参考github：https://github.com/burness/tensorflow-101/tree/master/chinese_hand_write_rec/src

import tensorflow as tf
import os
import random
import tensorflow.contrib.slim as slim
import time
import logging
import numpy as np
import pickle
from PIL import Image


logger = logging.getLogger('Training a chinese write char recognition')
logger.setLevel(logging.INFO)
# formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
logger.addHandler(ch)


tf.app.flags.DEFINE_boolean('random_flip_up_down', False, "Whether to random flip up down")
tf.app.flags.DEFINE_boolean('random_brightness', True, "whether to adjust brightness")
tf.app.flags.DEFINE_boolean('random_contrast', True, "whether to random constrast")

tf.app.flags.DEFINE_integer('charset_size', 3755, "Choose the first `charset_size` character to conduct our experiment.")
tf.app.flags.DEFINE_integer('image_size', 64, "Needs to provide same value as in training.")
tf.app.flags.DEFINE_boolean('gray', True, "whether to change the rbg to gray")
tf.app.flags.DEFINE_integer('max_steps', 12002, 'the max training steps ')
tf.app.flags.DEFINE_integer('eval_steps', 50, "the step num to eval")
tf.app.flags.DEFINE_integer('save_steps', 2000, "the steps to save")

tf.app.flags.DEFINE_string('checkpoint_dir', './checkpoint/', 'the checkpoint dir')
tf.app.flags.DEFINE_string('train_data_dir', './data/train/', 'the train dataset dir')
tf.app.flags.DEFINE_string('test_data_dir', './data/test/', 'the test dataset dir')
tf.app.flags.DEFINE_string('log_dir', './log', 'the logging dir')

tf.app.flags.DEFINE_boolean('restore', False, 'whether to restore from checkpoint')
tf.app.flags.DEFINE_boolean('epoch', 1, 'Number of epoches')
tf.app.flags.DEFINE_boolean('batch_size', 128, 'Validation batch size')
tf.app.flags.DEFINE_string('mode', 'train', 'Running mode. One of {"train", "valid", "test"}')
FLAGS = tf.app.flags.FLAGS


class DataIterator:
    def __init__(self, data_dir):
        # Set FLAGS.charset_size to a small value if available computation power is limited.
        truncate_path = data_dir + ('%05d' % FLAGS.charset_size)
        print(truncate_path)
        self.image_names = []
        for root, sub_folder, file_list in os.walk(data_dir):
            if root < truncate_path:
                self.image_names += [os.path.join(root, file_path) for file_path in file_list]
        random.shuffle(self.image_names)
        self.labels = [int(file_name[len(data_dir):].split(os.sep)[0]) for file_name in self.image_names]

    @property
    def size(self):
        retur