commit aa1c91780a3f8b6e5e6c8074a16a3dc9d92bb969
Author: xxxxxx <xxxx@.com>
Date:   Tue May 28 00:06:06 2019 +0800

    add

diff --git a/dataset/casia_hwdb.py b/dataset/casia_hwdb.py
new file mode 100644
index 0000000..a52add1
--- /dev/null
+++ b/dataset/casia_hwdb.py
@@ -0,0 +1,87 @@
+"""
+
+this is a wrapper handle CASIA_HWDB dataset
+since original data is complicated
+we using this class to get .png and label from raw
+.gnt data
+
+"""
+import struct
+import numpy as np
+import cv2
+
+
+class CASIAHWDBGNT(object):
+    """
+    A .gnt file may contains many images and charactors
+    """
+
+    def __init__(self, f_p):
+        self.f_p = f_p
+
+    def get_data_iter(self):
+        header_size = 10
+        with open(self.f_p, 'rb') as f:
+            while True:
+                header = np.fromfile(f, dtype='uint8', count=header_size)
+                if not header.size: 
+                    break
+                sample_size = header[0] + (header[1]<<8) + (header[2]<<16) + (header[3]<<24)
+                tagcode = header[5] + (header[4]<<8)
+                width = header[6] + (header[7]<<8)
+                height = header[8] + (header[9]<<8)
+                if header_size + width*height != sample_size:
+                    break
+                image = np.fromfile(f, dtype='uint8', count=width*height).reshape((height, width))
+                yield image, tagcode
+
+
+def resize_padding_or_crop(target_size, ori_img, padding_value=255):
+    if len(ori_img.shape) == 3:
+        res = np.zeros([ori_img.shape[0], target_size, target_size])
+    else:
+        res = np.ones([target_size, target_size])*padding_value
+        end_x = target_size
+        end_y = target_size
+        start_x = 0
+        start_y = 0
+        if ori_img.shape[0] < target_size:
+            end_x = int((target_size + ori_img.shape[0])/2)
+        if ori_img.shape[1] < target_size:
+            end_y = int((target_size + ori_img.shape[1])/2)
+        if ori_img.shape[0] < target_size:
+            start_x = int((target_size - ori_img.shape[0])/2)
+        if ori_img.shape[1] < target_size:
+            start_y = int((target_size - ori_img.shape[1])/2)
+        res[start_x:end_x, start_y:end_y] = ori_img
+        return np.array(res, dtype=np.uint8)
+
+if __name__ == "__main__":
+    gnt = CASIAHWDBGNT('samples/1001-f.gnt')
+
+    full_img = np.zeros([800, 800], dtype=np.uint8)
+    charset = []
+    i = 0
+    for img, tagcode in gnt.get_data_iter():
+        cv2.imshow('rr', img)
+        
+        try:
+            label = struct.pack('>H', tagcode).decode('gb2312')
+            cv2.waitKey(0)
+            print(label)
+            # img_padded = resize_padding_or_crop(80, img)
+            # col_idx = i%10
+            # row_idx = i//10
+            # full_img[row_idx*80:(row_idx+1)*80, col_idx*80:(col_idx+1)*80] = img_padded
+            # charset.append(label.replace('\x00', ''))
+            # if i >= 99:
+            #     cv2.imshow('rrr', full_img)
+            #     cv2.imwrite('sample.png', full_img)
+            #     cv2.waitKey(0)
+            #     print(charset)
+            #     break
+            # i += 1
+        except Exception as e:
+            # print(e.with_traceback(0))
+            print('decode error')
+            continue
diff --git a/readme.md b/readme.md
new file mode 100644
index 0000000..0be960b
--- /dev/null
+++ b/readme.md
@@ -0,0 +1,28 @@
+# TensorFlow 2.0 中文手写字识别
+
+本项目实现了基于CNN的中文手写字识别，并且采用标准的**tensorflow 2.0 api** 来构建！相比对简单的字母手写识别，本项目更能体现模型设计的精巧性和数据增强的熟练操作性，并且最终设计出来的模型可以直接应用于工业场合，比如 **票据识别**, **手写文本自动扫描** 等，相比于百度api接口或者QQ接口等，具有可优化性、免费性、本地性等优点。
+
+
+## Data
+
+在开始之前，先介绍一下本项目所采用的数据信息。我们的数据全部来自于CASIA的开源中文手写字数据集，该数据集分为两部分：
+
+- CASIA-HWDB：新版本的HWDB，我们仅仅使用1.0-1.2，这是单字的数据集，2.0-2.2是整张文本的数据集，我们暂时不用，单字里面包含了约7185个汉字以及171个英文字母、数字、标点符号等；
+- CASIA-OLHWDB：老版本的HWDB，格式一样，包含了约7185个汉字以及171个英文字母、数字、标点符号等。
+
+原始数据下载链接点击[这里](http://www.nlpr.ia.ac.cn/databases/handwriting/Offline_database.html).
+由于原始数据过于复杂，我们自己写了一个数据wrapper方便读取，统一将其转换为类似于Dataframe (Pandas)的格式，这样可以将一个字的特征和label方便的显示，也可以十分方便的将手写字转换为图片，采用CNN进行处理。这是我们展示的效果：
+
+<p align="center">
+<img src="https://s2.ax1x.com/2019/05/27/VeFtZq.md.png" />
+</p>
+
+其对应的label为：
+
+```
+['!', '"', '#', '$', '%', '&', '(', ')', '*', '+', ',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<', '=', '>', '?', '@', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '[', '\\', ']', '^', '`', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '{', '|', '}', '~', '、', '。', '々', '…', '‘', '’', '“', '”']
+```
+
+## Model
+
+关于我们采用的OCR模型的构建，我们大致采用的是比较先进的MobileNetV3架构，同时设计了一个修改的过的MobileNetV3Big的更深网络。主要考虑模型的轻量型和表达能力。最终训练结果表明，我们的模型可以在中文手写字上达到约99.8%的准确率。
\ No newline at end of file
diff --git a/samples/.gitignore b/samples/.gitignore
new file mode 100644
index 0000000..e5645d7
--- /dev/null
+++ b/samples/.gitignore
@@ -0,0 +1 @@
+1001-f.gnt
\ No newline at end of file
diff --git a/samples/001-f.gnt b/samples/001-f.gnt
new file mode 100644
index 0000000..b4ead5f
Binary files /dev/null and b/samples/001-f.gnt differ
diff --git a/samples/sample.png b/samples/sample.png
new file mode 100644
index 0000000..6a0f1e9
Binary files /dev/null and b/samples/sample.png differ
diff --git a/tests.py b/tests.py
new file mode 100644
index 0000000..e69de29