A Benchmark System for Indian Language Text Recognition

Tulsyan, Krishna; Srivastava, Nimisha; Mondal, Ajoy; Jawahar, C. V.

doi:10.1007/978-3-030-57058-3_6

Cited by 3 publications

(1 citation statement)

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“…It is observed that great success is achieved in scene text recognition in English and some Asian languages (e.g., Chinese). Script specific constraints, lack of benchmarking datasets and evaluation parameters are the major causes of less attainment in research outcomes for scene text recognition in Indic scripts [15]. Table 1.…”

Section: Review Of Scene Text Recognition Methodsmentioning

confidence: 99%

Recognition of Devanagari Scene Text Using Autoencoder CNN

Shiravale

Jayadevan²,

Sannakki³

2021

ELCVIA

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Scene text recognition is a well-rooted research domain covering a diverse application area. Recognition of scene text is challenging due to the complex nature of scene images. Various structural characteristics of the script also influence the recognition process. Text and background segmentation is a mandatory step in the scene text recognition process. A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique. Therefore, an attempt is made here to develop a robust foreground/background segmentation(separation) technique that produces the highest recognition results. A ground-truth dataset containing Devanagari scene text images is prepared for the experimentation. An encoder-decoder convolutional neural network model is used for text/background segmentation. The model is trained with Devanagari scene text images for pixel-wise classification of text and background. The segmented text is then recognized using an existing OCR engine (Tesseract). The word and character-level recognition rates are computed and compared with other existing segmentation techniques to establish the effectiveness of the proposed technique.

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Section: Review Of Scene Text Recognition Methodsmentioning

confidence: 99%