Amharic Text Image Recognition: Database, Algorithm, and Analysis

Belay, Birhanu Hailu; Habtegebirial, Tewodros; Liwicki, Marcus; Belay, Gebyehu; Stricker, Didier

doi:10.1109/icdar.2019.00205

Cited by 17 publications

(20 citation statements)

References 14 publications

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“…Many recent work ( [9], [10], [11], [12], [13] and [14]) use deep learning to automatically recognize offline handwritten texts in, respectively, Amharic, Mongolian, Latin, Bengla and Chinese languages. They mainly use convolutional and recurrent networks or a combination of both to perform the recognition task.…”

Section: Related Workmentioning

confidence: 99%

Integrating Writing Dynamics in CNN for Online Children Handwriting Recognition

Corbillé

Fromont

Anquetil

et al. 2020

2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR)

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Online handwriting recognition is challenging but an already well-studied topic. However, recent advances in the development of convolutional neural networks (CNN) make us believe that these networks could still improve the state of the art especially in the much more challenging context of online children handwritten letters recognition. This is because, children handwriting is, at an early stage of learning, approximate and includes deformed letters. To evaluate the potential of these networks, we study the early and late fusions of different input channels that can provide a CNN with information about the handwriting dynamics in addition to the static image of the characters. The experiments on a real children handwriting dataset with 27 000 characters acquired in primary schools, show that using multiple channels with CNN, improves the accuracy performance of different CNN architectures and different fusion settings for character recognition.

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Section: Related Workmentioning

confidence: 99%

Integrating Writing Dynamics in CNN for Online Children Handwriting Recognition

Corbillé

Fromont

Anquetil

et al. 2020

2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR)

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“…However, OCR gives better results only for very specific use cases and there are still multiple indigenous scripts, like Amharic, which are underrepresented in the area of natural language processing (NLP) and document image analysis [8]. Until recent times, the OCR for Amharic script remained relatively unexplored, and it is still challenging [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Nearly all of these attempts performed segmentation of text images into the character level, which also directly affects the performance of the OCR. The only exceptions are Assabie [21] and recently published works [9,24]. Assabie [21] proposed an Hidden Markov Model (HMM)-based model for offline handwritten Amharic word recognition without character segmentation by using the structural features of characters as building blocks of a recognition system, while Belay [9] and Addis [24] proposed Long-Short-Term Memory (LSTM) networks together with CTC (Connectionist Temporal Classification) for Amharic text image recognition.…”

Section: Introductionmentioning

confidence: 99%

“…The only exceptions are Assabie [21] and recently published works [9,24]. Assabie [21] proposed an Hidden Markov Model (HMM)-based model for offline handwritten Amharic word recognition without character segmentation by using the structural features of characters as building blocks of a recognition system, while Belay [9] and Addis [24] proposed Long-Short-Term Memory (LSTM) networks together with CTC (Connectionist Temporal Classification) for Amharic text image recognition. In literature, attempts at Amharic OCR have neither shown results on large datasets nor considered all possible characters used in the Amharic writing system [9].…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extension of the previous work [9] with the following summarized contributions: (1) To extract automatic features from text-line images, we propose a CNN-based feature extractor module. (2) To reduce the computational cost, we adjust the images to a smaller size.…”

Section: Introductionmentioning

confidence: 99%

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Amharic OCR: An End-to-End Learning

et al. 2020

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In this paper, we introduce an end-to-end Amharic text-line image recognition approach based on recurrent neural networks. Amharic is an indigenous Ethiopic script which follows a unique syllabic writing system adopted from an ancient Geez script. This script uses 34 consonant characters with the seven vowel variants of each (called basic characters) and other labialized characters derived by adding diacritical marks and/or removing parts of the basic characters. These associated diacritics on basic characters are relatively smaller in size, visually similar, and challenging to distinguish from the derived characters. Motivated by the recent success of end-to-end learning in pattern recognition, we propose a model which integrates a feature extractor, sequence learner, and transcriber in a unified module and then trained in an end-to-end fashion. The experimental results, on a printed and synthetic benchmark Amharic Optical Character Recognition (OCR) database called ADOCR, demonstrated that the proposed model outperforms state-of-the-art methods by 6.98% and 1.05%, respectively.

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