Diacritical Language OCR Based on Neural Network: Case of Amazigh Language

Gajoui, Khadija El; Allah, Fadoua Ataa; Oumsis, Mohammed

doi:10.1016/j.procs.2015.12.035

“…Furthermore, diacritic characters have been used for detecting image similarity in Quranic verses in [1]. Another work [5] discusses about diacritical language OCR and studies its behaviours with respect to conventional OCR. [11] talks about their segmentation-free approach where the characters and associated diacritics are detected separately with different networks.…”

Section: Related Workmentioning

confidence: 99%

On-Device Language Identification of Text in Images Using Diacritic Characters

Vatsal

¹

,

Arora

²

,

Ramena

³

et al. 2021

Communications in Computer and Information Science

1

0

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Diacritic characters can be considered as a unique set of characters providing us with adequate and significant clue in identifying a given language with considerably high accuracy. Diacritics, though associated with phonetics often serve as a distinguishing feature for many languages especially the ones with a Latin script. In this proposed work, we aim to identify language of text in images using the presence of diacritic characters in order to improve Optical Character Recognition (OCR) performance in any given automated environment. We showcase our work across 13 Latin languages encompassing 85 diacritic characters. We use an architecture similar to Squeezedet for object detection of diacritic characters followed by a shallow network to finally identify the language. OCR systems when accompanied with identified language parameter tends to produce better results than sole deployment of OCR systems. The discussed work apart from guaranteeing an improvement in OCR results also takes on-device (mobile phone) constraints into consideration in terms of model size and inference time.

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“…The experiments conducted proved that the chosen approach can achieve an average recognition rate of 98.73% on a typical database that contains 10 of the most popular Arabic fonts. The Amazigh language transcribed in Latin, which is distinguished by its diacritical characters, was studied by Gajoui et al [17]. They proposed a system based on neural networks and compared its behavior to a diacritical language and a diacritic-free language with a different quality paper.…”

Section: Related Workmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Lukasik,

Charytanowicz,

Milosz

et al. 2021

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Convolutional Neural Networks (CNN) have achieved huge popularity in solving problems in image analysis and in text recognition. In this work, we assess the effectiveness of CNN-based architectures where a network is trained in recognizing handwritten characters based on Latin script. European languages such as Dutch, French, German, etc., use different variants of the Latin script, so in the conducted research, the Latin alphabet was extended by certain characters with diacritics used in Polish language. To evaluate the recognition results under the same conditions, a handwritten Latin dataset was also developed. The proposed CNN architecture produced an accuracy of 96% for the extended character set. This is comparable to state-of-the-art results found in the domain of identifying handwritten characters. The presented approach extends the usage of CNN-based recognition to different variants of the Latin characters and shows it can be successfully used for a set of languages based on that script. It seems to be an effective technique for a set of languages written using the Latin script.

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“…Furthermore, diacritic characters have been used for detecting image similarity in Quranic verses in [1]. Another work [5] discusses about diacritical language OCR and studies its behaviours with respect to conventional OCR. [11] talks about their segmentationfree approach where the characters and associated diacritics are detected separately with different networks.…”

Section: Related Workmentioning

confidence: 99%

On-Device Language Identification of Text in Images using Diacritic Characters

Vatsal,

Arora,

Ramena

et al. 2020

Preprint

0

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Diacritic characters can be considered as a unique set of characters providing us with adequate and significant clue in identifying a given language with considerably high accuracy. Diacritics, though associated with phonetics often serve as a distinguishing feature for many languages especially the ones with a Latin script. In this proposed work, we aim to identify language of text in images using the presence of diacritic characters in order to improve Optical Character Recognition (OCR) performance in any given automated environment. We showcase our work across 13 Latin languages encompassing 85 diacritic characters. We use an architecture similar to Squeezedet for object detection of diacritic characters followed by a shallow network to finally identify the language. OCR systems when accompanied with identified language parameter tends to produce better results than sole deployment of OCR systems. The discussed work apart from guaranteeing an improvement in OCR results also takes on-device (mobile phone) constraints into consideration in terms of model size and inference time.

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Diacritical Language OCR Based on Neural Network: Case of Amazigh Language

Cited by 10 publications

References 5 publications

On-Device Language Identification of Text in Images Using Diacritic Characters

On-Device Language Identification of Text in Images Using Diacritic Characters

Bulletin of the Polish Academy of Sciences: Technical Sciences

On-Device Language Identification of Text in Images using Diacritic Characters

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