Semiautomatic Ground Truth Generation for Text Detection and Recognition in Video Images

Phan, Trung Quy; Shivakumara, Palaiahnakote; Bhowmick, Souvik; Li, Shimiao; Tan, Chew Lim; Pal, Umapada

doi:10.1109/tcsvt.2014.2305515

Cited by 9 publications

(2 citation statements)

References 31 publications

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“…Building an OCR engine is a very complicated and computationally expensive task. Hence many researchers have preferred existing OCR engines like Tesseract or ABBYY for experimentation/recognition purposes [40]. Numerous researchers have developed their own OCR systems by training their model with their dataset.…”

Section: Review Of Devanagari Document Text Recognition Methodsmentioning

confidence: 99%

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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 Devanagari Document Text Recognition Methodsmentioning

confidence: 99%

“…Adaptive development, inclusive of smart algorithms and extensive support for several languages, made them popular in the domain of recognition. As per the literature survey, Tesseract and ABBYY are the most popular OCR engines [40]. Tesseract OCR with Marathi/Hindi language trained model is used for the experimentation.…”

Section: Recognition By Ocr Enginementioning

confidence: 99%