如何使用CreateML正确训练OCR模型？我的不仅是坏的，而且毫无价值

Question

我创建了一个数据集，每个字母有1个生成的训练图像，每个字母约有10个现实测试图像。

所有像素均为10x14像素，黑白（在预处理阶段略微二进​​制化）。

生成的模型将所有符号识别为“ 1”（即使是测试集中的实际图像），因此基本上根本无法正常工作。

谁能指出我正确的方向？

这是CreateML输出-

Extracting image features from full data set.
Analyzing and extracting image features.
+------------------+--------------+------------------+
| Images Processed | Elapsed Time | Percent Complete |
+------------------+--------------+------------------+
| 1                | 1.74s        | 2.5%             |
| 2                | 1.96s        | 5.25%            |
| 3                | 2.17s        | 8%               |
| 4                | 2.39s        | 10.75%           |
| 5                | 2.60s        | 13.5%            |
| 10               | 3.68s        | 27%              |
| 25               | 6.90s        | 67.5%            |
| 37               | 9.48s        | 100%             |
| 36               | 9.26s        | 97.25%           |
+------------------+--------------+------------------+
Skipping automatic creation of validation set; training set has fewer than 50 points.
Beginning model training on processed features. 
Calibrating solver; this may take some time.
+-----------+--------------+-------------------+
| Iteration | Elapsed Time | Training Accuracy |
+-----------+--------------+-------------------+
| 0         | 0.038845     | 0.027027          |
| 1         | 0.139269     | 0.837838          |
| 2         | 0.268821     | 0.945946          |
| 3         | 0.317312     | 0.945946          |
| 4         | 0.367944     | 0.972973          |
| 5         | 0.422657     | 0.972973          |
| 10        | 0.713325     | 1.000000          |
| 24        | 1.495230     | 1.000000          |
+-----------+--------------+-------------------+
SUCCESS: Optimal solution found.
Extracting image features from evaluation data.
Analyzing and extracting image features.
+------------------+--------------+------------------+
| Images Processed | Elapsed Time | Percent Complete |
+------------------+--------------+------------------+
| 1                | 211.661ms    | 0.25%            |
| 2                | 425.538ms    | 0.75%            |
| 3                | 641.33ms     | 1.25%            |
| 4                | 861.215ms    | 1.75%            |
| 5                | 1.07s        | 2.25%            |
| 10               | 2.16s        | 4.75%            |
| 25               | 5.39s        | 12%              |
| 50               | 10.75s       | 24%              |
| 75               | 16.12s       | 36%              |
| 100              | 21.51s       | 48%              |
| 125              | 26.88s       | 60%              |
| 150              | 32.24s       | 72%              |
| 175              | 37.61s       | 84%              |
| 200              | 42.97s       | 96%              |
| 208              | 44.69s       | 100%             |
| 207              | 44.47s       | 99.5%            |
+------------------+--------------+------------------+
Trained model successfully saved at /mypath/ocr.mlmodel.

Answer 1

1. OCR很难。

2. 您没有足够的培训示例。

3. 创建ML使用非常强大的“功能提取器”。这也意味着该模型很容易简单地记住所有训练数据，因为图像很小，而且只有几个。要阻止这种情况的发生，您无法用Create ML做什么。

4. 尝试使用诸如scikit-learn之类的数据包对数据进行简单的逻辑回归训练。它比深度神经网络要好得多。