This repository contains our experiments with the deep learning library Keras and a subset of the CASIA-HWDB1.1 data set. The code here provided implements a convolutional neural network with test set accuracy greater than 95%. The network configuration is a simplified version of the one described in Deep Convolutional Network for Handwritten Chinese Character Recognition by Yuhao Zhang. No attempt was made to improve the accuracy of the model since we just wanted to play with convolutional networks and deep learning libraries.
Alessandro and Francesco
We assume you have already installed and configured Keras with any of its backends. Install the other required dependencies with:
$ pip2 install h5py numpy scipy Pillow scikit-image
The code was last tested on Keras 2.0.4 using Theano 0.9.0 as backend, h5py 2.7.0, numpy 1.12.1, scipy 0.19.0, Pillow 4.1.1 and scikit-image 0.13.0. Tests were run under a single GeForce GTX 970 using a floating-point precision of 32 bits.
In the releases section we uploaded a (zipped) subset of the CASIA-HWDB1.1 data set, the network model, network weights and some classifications, all generated following the steps below. If you use our subset, start from step 3.
- Download the CASIA-HWDB1.1 data set from the official locations (HWDB1.1trn_gnt.zip (1873 MB) and HWDB1.1tst_gnt.zip (471 MB)) and unzip it (it is required to decompress an archive in ALZ format):
$ unzip HWDB1.1trn_gnt.zip
Archive: HWDB1.1trn_gnt.zip
inflating: HWDB1.1trn_gnt.alz
$ unalz HWDB1.1trn_gnt.alz
unalz v0.65 (2009/04/01)
Copyright(C) 2004-2009 by [email protected] (http://www.kipple.pe.kr)
Extract HWDB1.1trn_gnt.alz to .
unalziiiing : 1219-c.gnt (21547829bytes) ...........
unalziiiing : 1220-c.gnt (26773966bytes) ...........
[...]
$ mkdir HWDB1.1trn_gnt
$ mv *.gnt HWDB1.1trn_gnt/
$ unzip HWDB1.1tst_gnt.zip
Archive: HWDB1.1tst_gnt.zip
inflating: 1289-c.gnt
inflating: 1290-c.gnt
[...]
$ mkdir HWDB1.1tst_gnt
$ mv *.gnt HWDB1.1tst_gnt/
- Convert the data set into the HDF5 binary data format:
$ python2 1-gnt_to_dataset.py HWDB1.1trn_gnt/ HWDB1.1tst_gnt/
Converting 'trn'...
Converting 'tst'...
- Extract from the HDF5 data set a subset of 200 classes of characters:
$ python2 2-dataset_to_subset.py HWDB1.1.hdf5
Subsetting 'trn'...
Subsetting 'tst'...
- Train the network on the subset:
$ python2 3-train_subset.py HWDB1.1subset.hdf5
Using Theano backend.
Using gpu device 0: GeForce GTX 970 (CNMeM is disabled, cuDNN not available)
Train on 40000 samples, validate on 8020 samples
Epoch 1/15
40000/40000 [==============================] - 65s - loss: 3.5933 - acc: 0.2570 - val_loss: 1.0810 - val_acc: 0.7385
Epoch 2/15
40000/40000 [==============================] - 65s - loss: 1.0313 - acc: 0.7361 - val_loss: 0.5121 - val_acc: 0.8686
Epoch 3/15
40000/40000 [==============================] - 65s - loss: 0.5632 - acc: 0.8469 - val_loss: 0.3775 - val_acc: 0.8994
Epoch 4/15
40000/40000 [==============================] - 66s - loss: 0.3812 - acc: 0.8923 - val_loss: 0.3912 - val_acc: 0.8964
Epoch 5/15
40000/40000 [==============================] - 67s - loss: 0.2735 - acc: 0.9218 - val_loss: 0.2458 - val_acc: 0.9374
Epoch 6/15
40000/40000 [==============================] - 67s - loss: 0.2164 - acc: 0.9356 - val_loss: 0.2603 - val_acc: 0.9330
Epoch 7/15
40000/40000 [==============================] - 67s - loss: 0.1749 - acc: 0.9469 - val_loss: 0.2283 - val_acc: 0.9389
Epoch 8/15
40000/40000 [==============================] - 66s - loss: 0.1402 - acc: 0.9570 - val_loss: 0.2030 - val_acc: 0.9507
Epoch 9/15
40000/40000 [==============================] - 66s - loss: 0.1217 - acc: 0.9625 - val_loss: 0.2070 - val_acc: 0.9481
Epoch 10/15
40000/40000 [==============================] - 66s - loss: 0.0981 - acc: 0.9690 - val_loss: 0.2097 - val_acc: 0.9481
Epoch 11/15
40000/40000 [==============================] - 65s - loss: 0.0885 - acc: 0.9727 - val_loss: 0.2145 - val_acc: 0.9485
Epoch 12/15
40000/40000 [==============================] - 65s - loss: 0.0826 - acc: 0.9734 - val_loss: 0.2091 - val_acc: 0.9522
Epoch 13/15
40000/40000 [==============================] - 66s - loss: 0.0703 - acc: 0.9779 - val_loss: 0.1951 - val_acc: 0.9541
Epoch 14/15
40000/40000 [==============================] - 66s - loss: 0.0665 - acc: 0.9787 - val_loss: 0.2239 - val_acc: 0.9484
Epoch 15/15
40000/40000 [==============================] - 66s - loss: 0.0568 - acc: 0.9820 - val_loss: 0.2226 - val_acc: 0.9519
Test score: 0.189350152647
Test accuracy: 0.95839959825
- (Optional) Generate a report of some classifications:
$ python2 4-draw_results.py HWDB1.1subset.hdf5 model.json weights.hdf5
Using Theano backend.
Using gpu device 0: GeForce GTX 970 (CNMeM is disabled, cuDNN not available)
Evaluating the network on the test set...
Test score: 0.189350152647
Test accuracy: 0.95839959825
Extracting some results...
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