Abstract:
Amharic (Amarñña: አማርኛ) is the official language of the Federal Government of
Ethiopia, with more than 27 million speakers. But it is a low-resourced language, and
only a few attempts have been made so far for handwritten Amharic text recognition.
This is challenging due to the very high similarity between many of the alphabets, and
handwriting calligraphy is personal. This paper presents offline handwritten Amharic
word recognition using deep learning architecture, which comprises convolutional
neural networks (CNNs) for feature extraction from input word images, bidirectional
recurrent neural networks (BRNNs) for sequence encoding, moreover connectionist
temporal classification (CTC) as a loss function.
To the authors knowledge, there was no any publicly available handwritten Amharic
text dataset. Therefore, we commenced from preparing the dataset from scratch. We
chose A* path-planning and scale space algorithms respectively for line and word
level segmentation from the raw handwritten text image. We have collected 12 064
word-images for our dataset. Data augmentation was employed by applying random
transformations to the word-images of the training set to enhance performance of the
proposed models.
In the main process, we have developed a custom model with CNN-BRNN-CTC
framework, and Bayesian algorithm was used to set values for hyper-parameters.
We have then compared four different state-of-the-art CNN models: EfficientNet,
DenseNet, ResNet and VGG for robust feature extraction. We did this experiment by
modifying their architectures to fit our problem domain. We have measured the word
error rate (WER) and character error rate (CER) using our test set, which contains
1200 word images (they are randomly selected 10% of the total dataset). Hence,
the outperforming model with DenseNet201 feature extractor network has achieved
WER of 9.00 % and CER of 2.51 % on the non-augmented 64 × 256 word-image
dataset and become advanced in WER of 6.83 % and CER of 1.82 % on augmented
one. Whereas, the custom model has also achieved competitive performance on the
offline handwritten Amharic word recognition with the state-of-the-art models
