Abstract
Smart automated management and access to ever increasing number of scene text images is a pressing need to enable individuals and organizations save time and energy. Text embedded in such an image is an important descriptor of the image itself. In this paper, a novel scheme for automatic generation of annotations by OCRing scene text images is presented and the performance is demonstrated on a smart infobase, a knowledge base designed with trie data structure. A neuro-fuzzy approach is used for text detection and multi-layer perceptron is incorporated for character recognition. Appropriate post-processing has increased the classification performance from 90.73% to 96.86% (i.e. higher than Tesseract 3.01 that yields 93.51%). Q-gram based index keys are generated from the OCR’d text and indexed in the infobase enabling appropriate relevance scoring. Besides ‘query text’, the system also supports ‘query image’. The retrieval engine returns scene images in order of relevance i.e. in decreasing order. The performance is successfully demonstrated on a set of 100 camera captured scene text images. The system works satisfactorily within the present scope of applications.
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Mollah, A.F., Basu, S., Nasipuri, M. (2018). An Automatic Annotation Scheme for Scene Text Archival Applications. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_7
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DOI: https://doi.org/10.1007/978-981-13-1813-9_7
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