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About Deep Learning For Amharic Text-ImageRecognition
This thesis explores deep learning techniques for optical character recognition (OCR) of Amharic text images. It introduces a new dataset of Amharic images annotated at the character and text-line levels. It presents a novel convolutional neural network (CNN) model that achieves 94.97% character recognition accuracy. It also investigates sequence-to-sequence models for text-line recognition and proposes an attention-based model that outperforms existing techniques with a character error rate of 1.04% on printed text and 0.93% on synthetic text. The thesis addresses challenges in Amharic OCR through algorithmic contributions of suitable deep learning approaches and technical contributions including the new dataset and high-performing models.
Detailed Information
| Author: | ['engidaw awoke'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 158 |
| Language: | English |
| Format: | |
| Price: | FREE |
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