Machine reading of camera-held low quality text images: An ICA-based image enhancement approach for improving OCR accuracy

Garain, Utpal; Jain, Ankush; Maity, Abhishek; Chanda, Bhabatosh

doi:10.1109/icpr.2008.4761840

Cited by 20 publications

(17 citation statements)

References 8 publications

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“…The enhanced outputs of India Gate images using the proposed method are shown in Figures 9 and 10. We have also compared the proposed method with Fast ICA based enhancement [3], and results are shown in Table 1 in Figure 4. Other results of Hampi inscription images are shown in Figures 5 and 11.…”

Section: Resultsmentioning

confidence: 99%

“…These images can be recognized by OCR only after proper enhancement. Fast ICA [3] based enhancement method has given good results for inscription images with a reasonable colour difference between text and the background. Most of the ancient inscriptions do not have such reasonable colour distinction between the two regions.…”

Section: Introductionmentioning

confidence: 99%

“…Garain et al [3] describe how to enhance image using Fast ICA algorithm which results in three independent components or layers which correspond to the contribution of text in them. The method is an enhancement method, which; however, is unable to enhance inscription images which are weak or highly spatially correlated sources.…”

Section: Related Workmentioning

confidence: 99%

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NGFICA Based Digitization of Historic Inscription Images

Indu

Pandey

Jayanthi

et al. 2013

ISRN Signal Processing

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This paper addresses the problems encountered during digitization and preservation of inscriptions such as perspective distortion and minimal distinction between foreground and background. In general inscriptions possess neither standard size and shape nor colour difference between the foreground and background. Hence the existing methods like variance based extraction and Fast ICA based analysis fail to extract text from these inscription images. Natural gradient flexible ICA (NGFICA) is a suitable method for separating signals from a mixture of highly correlated signals, as it minimizes the dependency among the signals by considering the slope of the signal at each point. We propose an NGFICA based enhancement of inscription images. The proposed method improves word and character recognition accuracies of the OCR system by 65.3% (from 10.1% to 75.4%) and 54.3% (from 32.4% to 86.7%), respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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NGFICA Based Digitization of Historic Inscription Images

Indu

Pandey

Jayanthi

et al. 2013

ISRN Signal Processing

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“…These images can be recognized by OCR only after proper enhancement. FastlCA [3] based enhancement method has given good results for inscription 978-1-4673-5952-8/13/$31.00 © 2013 IEEE images with a reasonable colour difference between text and the background. Most of the ancient inscriptions do not have such reasonable colour distinction between the two regions.…”

Section: Introductionmentioning

confidence: 95%

Enhancement of inscription images

Indu

Pandey

Jayanthi

et al. 2013

2013 National Conference on Communications (NCC)

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This paper addresses the problems encountered during digitization and preservation of inscriptions such as per spective distortion and minimal distinction between foreground and background. In general inscriptions neither possess standard size and shape nor colour difference between the foreground and background. Hence the existing methods like variance based extraction and Fast-ICA based analysis fail to extract text from these inscription images. Natural gradient Flexible ICA (NGFICA) is a suitable method for separating signals from a mixture of highly correlated signals, as it minimizes the dependency among the signals by considering the slope of the signal at each point. We propose an NGFICA based enhancement of inscription images. The proposed method improves word and character recognition accuracies of the OCR system by 65.3% (from 10.1% to 75.4%) and 54.3% (from 32.4% to 86.7%) respectively.

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“…They employed support vector machine framework for implementation [13]. Jain et al, applied independent component analysis method on the un-calibrated camera-based image [14] and showed significant improvement on OCR recognition.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of OCR Correction Model for Numeric Digits based on a Context Sensitive and Multiple Streams

2011

The KIPS Transactions:PartD

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On an automated business document processing system maintaining financial data, errors on query based retrieval of numbers are critical to overall performance and usability of the system. Automatic spelling correction methods have been emerged and have played important role in development of information retrieval system. However scope of the methods was limited to the symbols, for example alphabetic letter strings, which can be reserved in the form of trainable templates or custom dictionary. On the other hand, numbers, a sequence of digits, are not the objects that can be reserved into a dictionary but a pure markov sequence. In this paper we proposed a new OCR model for spelling correction for numbers using the multiple streams and the context based correction on top of probabilistic information retrieval framework.We implemented the proposed error correction model as a sub-module and integrated into an existing automated invoice document processing system. We also presented the comparative test results that indicated significant enhancement of overall precision of the system by our model.

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Machine reading of camera-held low quality text images: An ICA-based image enhancement approach for improving OCR accuracy

Cited by 20 publications

References 8 publications

NGFICA Based Digitization of Historic Inscription Images

NGFICA Based Digitization of Historic Inscription Images

Enhancement of inscription images

Design and Implementation of OCR Correction Model for Numeric Digits based on a Context Sensitive and Multiple Streams

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