Use TensorBoard - it provides visualizations for model performance over time. The model is not learning if val_loss (validation loss) goes up or does not decrease as the training goes on.
dropouts), data augmentation, improvement on quality of the dataset How to know whether the model is “learning” Some solutions to overfitting: regularization (ex. How to detect overfitting: acc (accuracy) continues to go up, but the val_acc (validation accuracy) does the opposite in the training process. Check out this article for intuitive explanations. This means that the model is not well generalized. Here are some tips if you find the performance of the model unsatisfactory in the training step: Model is overfitting Nb_epoch=30, validation_data=(X_test, y_test)) Model.fit_generator(datagen.flow(X_train, y_train, batch_size=16), Model.add(MaxPooling2D(pool_size=(2, 2))) Model.add(Conv2D(32, 3, 3, input_shape=input_shape))
Feel free to play around with different parameters yourself. The combination below gave me the highest accuracy - 98.77%. When I first built the model, I experimented with hyper-parameters and tuned them multiple times.
Now comes in the fun part! We will use Keras to construct a CNN (Convolutional Neural Network) for our model. Y_test = np_utils.to_categorical(y_test, nb_classes)ĭatagen = ImageDataGenerator(rotation_range=15, zoom_range=0.20)ĭatagen.fit(X_train) Build and Train the CNN ?️
Y_train = np_utils.to_categorical(y_train, nb_classes) # convert class vectors to categorical matrices X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2) Y_train = np.repeat(np.arange(nb_classes), 160) X_train = (ary, (img_rows, img_cols), mode='F') import scipy.miscįrom keras.layers import Conv2D, MaxPooling2Dįrom keras.layers import Dense, Dropout, Activation, Flattenįrom import ImageDataGeneratorįrom sklearn.model_selection import train_test_splitĪry = np.load("hiragana.npz").reshape().astype(np.float32) / 15 When our model is trained on character images from a variety of angles, our model can better adapt to people’s handwriting. We will also add data augmentation to generate extra images that are rotated and zoomed. Once we have hiragana.npz saved, let’s start processing images by loading the file and reshaping the image dimensions to 32x32 pixels. If not b'KAI' in r and not b'HEI' in r:Īry = np.array(r) To get the images from the database, we need some helper functions that read and store images in.
Images of handwritten “あ” produced by 160 writers (from ETL8) Since we are going to build an OCR for Hiragana, ETL8 is the dataset we will use. The dataset comes from the ETL Character Database, which contains nine sets of images of handwritten characters and symbols. Part 3️⃣: Integrate the trained model into iOS What the final app could look like (demo comes from Recogmize) Obtain the dataset & Preprocess Images ? Part 2️⃣: Build & train the CNN (Convolutional Neural Network) Part 1️⃣: Obtain the dataset and preprocess images Without further ado, let’s take a look at what will be covered: For those who would like to build one for other languages/symbols, feel free to customize it by changing the dataset. This article will break down the process of how we built a Japanese OCR for iOS apps. We learned a lot throughout the process, but more importantly, we were thrilled to ship a better product for our users. Users can be deep in the mountains without the internet and still open up Nukon to maintain their daily routine of learning Japanese. The biggest benefit we got from building one from scratch was that ours works offline. We had no choice but to build our own OCR.
Basic Hiragana & Katakanaĭuring the development of Nukon, there was no API available for handwriting recognition in Japanese. We then collaborated to build something more sophisticated than a digit recognizer: an OCR (Optical Character Recognition/Reader) for Japanese characters ( Hiragana and Katakana). He coincidentally wanted to have a similar feature in it. My friend Kaichi Momose was developing a Japanese language learning app, Nukon. While I was learning how to create deep learning models for the MNIST dataset a few months ago, I ended up making an iOS app that recognized handwritten characters. From constructing a Convolutional Neural Network to deploying an OCR to iOS The Motivation for the Project ✍️ ?