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Learning New Vocabulary Implicitly During Sleep Transfers With Cross-Modal Generalization Into Wakefulness
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In: ISSN: 1662-4548 ; EISSN: 1662-453X ; Frontiers in Neuroscience ; https://hal.sorbonne-universite.fr/hal-03640595 ; Frontiers in Neuroscience, Frontiers, 2022, 16, pp.801666. ⟨10.3389/fnins.2022.801666⟩ (2022)
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Still I Aspire: Graduate Degree Aspirations for Community College Transfer Students of Color
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MAGIC DUST FOR CROSS-LINGUAL ADAPTATION OF MONOLINGUAL WAV2VEC-2.0
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In: ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03544515 ; ICASSP 2022, May 2022, Singapour, Singapore (2022)
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Cross-lingual few-shot hate speech and offensive language detection using meta learning
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In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559484 ; IEEE Access, IEEE, 2022, 10, pp.14880-14896. ⟨10.1109/ACCESS.2022.3147588⟩ (2022)
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Machine-readable Finnish-Livvi bilingual translation dictionary ...
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Machine-readable Finnish-Karelian bilingual translation dictionary ...
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Machine-readable Finnish-Karelian bilingual translation dictionary ...
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Machine-readable Northern Karelian Proper-Livvi bilingual translation dictionary ...
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Machine-readable Finnish-Livvi bilingual translation dictionary ...
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Machine-readable Northern Karelian Proper-Livvi bilingual translation dictionary ...
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TEACHING ENGLISH FOR THE SECOND LANGUAGE STUDENTS AS THE SECOND LANGUAGE ...
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TEACHING ENGLISH FOR THE SECOND LANGUAGE STUDENTS AS THE SECOND LANGUAGE ...
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ECONOMIC TERMS IN THE LEXICAL SYSTEM OF THE MODERN UZBEK LANGUAGE ...
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ECONOMIC TERMS IN THE LEXICAL SYSTEM OF THE MODERN UZBEK LANGUAGE ...
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Visual generics: How children understand generic language with different visualizations ...
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Attributions of Successful English Language Learners in Transfer-Level English
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In: Doctoral Dissertations and Projects (2022)
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Improving Scene Text Recognition for Indian Languages with Transfer Learning and Font Diversity
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In: Journal of Imaging; Volume 8; Issue 4; Pages: 86 (2022)
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Abstract:
Reading Indian scene texts is complex due to the use of regional vocabulary, multiple fonts/scripts, and text size. This work investigates the significant differences in Indian and Latin Scene Text Recognition (STR) systems. Recent STR works rely on synthetic generators that involve diverse fonts to ensure robust reading solutions. We present utilizing additional non-Unicode fonts with generally employed Unicode fonts to cover font diversity in such synthesizers for Indian languages. We also perform experiments on transfer learning among six different Indian languages. Our transfer learning experiments on synthetic images with common backgrounds provide an exciting insight that Indian scripts can benefit from each other than from the extensive English datasets. Our evaluations for the real settings help us achieve significant improvements over previous methods on four Indian languages from standard datasets like IIIT-ILST, MLT-17, and the new dataset (we release) containing 440 scene images with 500 Gujarati and 2535 Tamil words. Further enriching the synthetic dataset with non-Unicode fonts and multiple augmentations helps us achieve a remarkable Word Recognition Rate gain of over 33% on the IIIT-ILST Hindi dataset. We also present the results of lexicon-based transcription approaches for all six languages.
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Keyword:
Indian languages; indic OCR; multi-lingual OCR; non-Unicode fonts; photo OCR; scene text recognition; synthetic data; transfer learning
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URL: https://doi.org/10.3390/jimaging8040086
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Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface
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In: Sensors; Volume 22; Issue 2; Pages: 535 (2022)
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