Abstract
In this paper we propose a framework for recognition and retrieval tasks in the context of scene text images. In contrast to many of the recent works, we focus on the case where an image-specific list of words, known as the small lexicon setting, is unavailable. We present a conditional random field model defined on potential character locations and the interactions between them. Observing that the interaction potentials computed in the large lexicon setting are less effective than in the case of a small lexicon, we propose an iterative method, which alternates between finding the most likely solution and refining the interaction potentials. We evaluate our method on public datasets and show that it improves over baseline and state-of-the-art approaches. For example, we obtain nearly 15 % improvement in recognition accuracy and precision for our retrieval task over baseline methods on the IIIT-5K word dataset, with a large lexicon containing 0.5 million words.
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Notes
- 1.
It should also be noted that [7] follows an open vocabulary lexicon, i.e., it does not assume that the ground truth is present in the lexicon. We find that around 75 % of the ground truth words from the IIIT 5K-word dataset are present in the large lexicon by default. The rest of the ground truth words are language-specific and proper nouns like city and shop names.
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Acknowledgements
This work was partially supported by the Ministry of Communications and Information Technology, Government of India, New Delhi. Anand Mishra was supported by Microsoft Corporation and Microsoft Research India under the Microsoft Research India PhD fellowship award.
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Roy, U., Mishra, A., Alahari, K., Jawahar, C.V. (2015). Scene Text Recognition and Retrieval for Large Lexicons. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_32
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