Benchmarking recognition results on camera captured word image data sets

Kumar, Deepak and Prasad, Anil MN and Ramakrishnan, AG (2012) Benchmarking recognition results on camera captured word image data sets. In: Proceeding of the workshop on Document Analysis and Recognition, Dec. 16, 2012, New York, NY, USA.

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Abstract

We have benchmarked the maximum obtainable recognition accuracy on five publicly available standard word image data sets using semi-automated segmentation and a commercial OCR. These images have been cropped from camera captured scene images, born digital images (BDI) and street view images. Using the Matlab based tool developed by us, we have annotated at the pixel level more than 3600 word images from the five data sets. The word images binarized by the tool, as well as by our own midline analysis and propagation of segmentation (MAPS) algorithm are recognized using the trial version of Nuance Omnipage OCR and these two results are compared with the best reported in the literature. The benchmark word recognition rates obtained on ICDAR 2003, Sign evaluation, Street view, Born-digital and ICDAR 2011 data sets are 83.9%, 89.3%, 79.6%, 88.5% and 86.7%, respectively. The results obtained from MAPS binarized word images without the use of any lexicon are 64.5% and 71.7% for ICDAR 2003 and 2011 respectively, and these values are higher than the best reported values in the literature of 61.1% and 41.2%, respectively. MAPS results of 82.8% for BDI 2011 dataset matches the performance of the state of the art method based on power law transform.

Item Type:	Conference Paper
Publisher:	ACM, Inc
Additional Information:	Copyright of this article belongs to ACM, Inc.
Keywords:	Word Images; Pixel Level Annotation; MAST-CH Toolkit; Cropped Word Recognition; Benchmarking; Scene Images; Born-Digital Images; Sign Evaluation
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	02 Jul 2013 07:52
Last Modified:	02 Jul 2013 07:52
URI:	http://eprints.iisc.ac.in/id/eprint/46548

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