307 research outputs found
Handwritten Amharic Character Recognition Using a Convolutional Neural Network
Amharic is the official language of the Federal Democratic Republic of Ethiopia. There are lots of historic Amharic and Ethiopic handwritten documents addressing various relevant issues including governance, science, religious, social rules, cultures and art works which are very rich indigenous knowledge. The Amharic language has its own alphabet derived from Ge’ez which is currently the liturgical language in Ethiopia. Handwritten character recognition for non Latin scripts like Amharic is not addressed especially using the advantages of state-of-the-art techniques. This research work designs for the first time a model for Amharic handwritten character recognition using a convolutional neural network. The dataset was organized from collected sample handwritten documents and data augmentation was applied for machine learning
Handwritten Arabic Character Recognition for Children Writ-ing Using Convolutional Neural Network and Stroke Identification
Automatic Arabic handwritten recognition is one of the recently studied
problems in the field of Machine Learning. Unlike Latin languages, Arabic is a
Semitic language that forms a harder challenge, especially with variability of
patterns caused by factors such as writer age. Most of the studies focused on
adults, with only one recent study on children. Moreover, much of the recent
Machine Learning methods focused on using Convolutional Neural Networks, a
powerful class of neural networks that can extract complex features from
images. In this paper we propose a convolutional neural network (CNN) model
that recognizes children handwriting with an accuracy of 91% on the Hijja
dataset, a recent dataset built by collecting images of the Arabic characters
written by children, and 97% on Arabic Handwritten Character Dataset. The
results showed a good improvement over the proposed model from the Hijja
dataset authors, yet it reveals a bigger challenge to solve for children Arabic
handwritten character recognition. Moreover, we proposed a new approach using
multi models instead of single model based on the number of strokes in a
character, and merged Hijja with AHCD which reached an averaged prediction
accuracy of 96%.Comment: 1
Does color modalities affect handwriting recognition? An empirical study on Persian handwritings using convolutional neural networks
Most of the methods on handwritten recognition in the literature are focused
and evaluated on Black and White (BW) image databases. In this paper we try to
answer a fundamental question in document recognition. Using Convolutional
Neural Networks (CNNs), as eye simulator, we investigate to see whether color
modalities of handwritten digits and words affect their recognition accuracy or
speed? To the best of our knowledge, so far this question has not been answered
due to the lack of handwritten databases that have all three color modalities
of handwritings. To answer this question, we selected 13,330 isolated digits
and 62,500 words from a novel Persian handwritten database, which have three
different color modalities and are unique in term of size and variety. Our
selected datasets are divided into training, validation, and testing sets.
Afterwards, similar conventional CNN models are trained with the training
samples. While the experimental results on the testing set show that CNN on the
BW digit and word images has a higher performance compared to the other two
color modalities, in general there are no significant differences for network
accuracy in different color modalities. Also, comparisons of training times in
three color modalities show that recognition of handwritten digits and words in
BW images using CNN is much more efficient
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