Using the Google Web 1T 5-Gram Corpus for OCR Error Correction

Cacho, Jorge Ramón Fonseca; Taghva, Kazem; Álvarez, Daniel

doi:10.1007/978-3-030-14070-0_71

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…[25] proposed one of the first CNN based approach for text recognition to classify words into fixed set of character texts. [26] uses an n-gram model to correct the OCR text extracted. [27] performed a comprehensive literature review on handwriting character recognition.…”

Section: Related Workmentioning

confidence: 99%

SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!

Sharma¹,

Bhageria²,

Scott³

et al. 2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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Information on social media comprises of various modalities such as textual, visual and audio. NLP and Computer Vision communities often leverage only one prominent modality in isolation to study social media. However, computational processing of Internet memes needs a hybrid approach. The growing ubiquity of Internet memes on social media platforms such as Facebook, Instagram, and Twitter further suggests that we can not ignore such multimodal content anymore. To the best of our knowledge, there is not much attention towards meme emotion analysis. The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes. The task Memotion analysis released approx 10K annotated memes-with human annotated labels namely sentiment(positive, negative, neutral), type of emotion(sarcastic,funny,offensive, motivation) and their corresponding intensity. The challenge consisted of three subtasks: sentiment (positive, negative, and neutral) analysis of memes, overall emotion (humor, sarcasm, offensive, and motivational) classification of memes, and classifying intensity of meme emotion. The best performances achieved were F 1 (macro average) scores of 0.35, 0.51 and 0.32, respectively for each of the three subtasks.

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Section: Related Workmentioning

confidence: 99%