The Ethiopic Script: Linguistic Features and Socio-cultural Connotations

Meyer, Ronny

doi:10.5617/osla.4422

Cited by 2 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Ethiopic script originated from the Geez script, one of the world's ancient scripts [13]. It is used as a writing system for more than 43 languages, including Amharic, Geez, and Tigrigna.…”

Section: Ethiopic Scriptmentioning

confidence: 99%

Gated Convolution and Stacked Self-Attention Encoder–Decoder-Based Model for Offline Handwritten Ethiopic Text Recognition

Tadesse,

Liu,

2023

Information

View full text Add to dashboard Cite

Offline handwritten text recognition (HTR) is a long-standing research project for a wide range of applications, including assisting visually impaired users, humans and robot interactions, and the automatic entry of business documents. However, due to variations in writing styles, visual similarities between different characters, overlap between characters, and source document noise, designing an accurate and flexible HTR system is challenging. The problem becomes serious when the algorithm has a low learning capacity and when the text used is complex and has a lot of characters in the writing system, such as Ethiopic script. In this paper, we propose a new model that recognizes offline handwritten Ethiopic text using a gated convolution and stacked self-attention encoder–decoder network. The proposed model has a feature extraction layer, an encoder layer, and a decoder layer. The feature extraction layer extracts high-dimensional invariant feature maps from the input handwritten image. Using the extracted feature maps, the encoder and decoder layers transcribe the corresponding text. For the training and testing of the proposed model, we prepare an offline handwritten Ethiopic text-line dataset (HETD) with 2800 samples and a handwritten Ethiopic word dataset (HEWD) with 10,540 samples obtained from 250 volunteers. The experiment results of the proposed model on HETD show a 9.17 and 13.11 Character Error Rate (CER) and Word Error Rate (WER), respectively. However, the model on HEWD shows an 8.22 and 9.17 CER and WER, respectively. These results and the prepared datasets will be used as a baseline for future research.

show abstract

Section: Ethiopic Scriptmentioning

confidence: 99%

Gated Convolution and Stacked Self-Attention Encoder–Decoder-Based Model for Offline Handwritten Ethiopic Text Recognition

Tadesse,

Liu,

2023

Information

View full text Add to dashboard Cite

show abstract

“…Our proposed model's performance is assessed using character error rate (CER). The CER is calculated using equation (1).…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…South Arabian alphabet [1], [2]. With a history spanning over two thousand years, the Ethiopic script is still in use today, making it one of the few writing systems that has stood the test of time.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Historical Handwritten Ethiopic Text Recognition Using Deep Learning

Malhotra,

Addis

2023

IEEE Access

View full text Add to dashboard Cite

Recognizing handwritten text is a challenging task, especially for scripts with numerous alphabets and symbols. The Ethiopic script has a vast character set and is used for historical documents in typewritten, handwritten, and hand-printed forms. However, despite its importance as an ancient script, optical character recognition research has not given enough attention to Ethiopic text recognition. In recent years, deep learning (DL) has emerged as a powerful technique for recognizing patterns. In this study, a DL approach is used to recognize historical Ethiopic handwritten texts. The recognition model employs an endto-end strategy enabling sequential feature extraction and efficient recognition. An attention mechanism coupled with a connectionist temporal classification architecture is the core of this recognition model architecture. In addition, there are seven convolutional neural networks and two recurrent neural networks. We increase the training data using data augmentation techniques to address the data scarcity common in deep learning applications. The experiments include an original training dataset of 79,684 historical handwritten images and an augmented dataset of 10,000 images containing Ethiopic texts. The model used for recognition showed promising results. For ''Test Set I'' which had 6,150 samples, the character error rate (CER) was 17.95%, and for ''Test Set II'' which had 15,935 samples, the CER was 29.95%. These outcomes indicate that this approach has the potential to improve the recognition of historical handwritten Ethiopic text.INDEX TERMS Deep learning, end-to-end learning, ethiopic script, handwritten text recognition, pattern recognition.RUCHIKA MALHOTRA received the master's and Ph.D. degrees in software engineering from

show abstract

The Ethiopic Script: Linguistic Features and Socio-cultural Connotations

Cited by 2 publications

References 5 publications

Gated Convolution and Stacked Self-Attention Encoder–Decoder-Based Model for Offline Handwritten Ethiopic Text Recognition

Gated Convolution and Stacked Self-Attention Encoder–Decoder-Based Model for Offline Handwritten Ethiopic Text Recognition

End-to-End Historical Handwritten Ethiopic Text Recognition Using Deep Learning

Contact Info

Product

Resources

About