MIRACLE-GSI at ImageCLEFphoto 2008: Different Strategies for Automatic Topic Expansion

Villena-Román, Julio; Lana-Serrano, Sara; González-Cristóbal, José Carlos

doi:10.1007/978-3-642-04447-2_70

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…On the other hand, the core recognition module is generic in nature, and we have sufficient data to train that module robustly. The use of hybrid rule-based and machine learning systems is a common practice in the area of pattern recognition (e.g., [42][43][44]), including document processing (e.g., [21]). For the courtesy amount recognition module, we propose two different approaches, one segmentation-based and one segmentation-free approach and compare their performances.…”

Section: A System For Arabic Check Courtesy Amount Recognitionmentioning

confidence: 99%

A Hybrid Rule-Based and Machine Learning System for Arabic Check Courtesy Amount Recognition

Ahmad

2023

Sensors

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Courtesy amount recognition from bank checks is an important application of pattern recognition. Although much progress has been made on isolated digit recognition for Indian digits, there is no work reported in the literature on courtesy amount recognition for Arabic checks using Indian digits. Arabic check courtesy amount recognition comes with its own unique challenges that are not seen in isolated digit recognition tasks and, accordingly, need specific approaches to deal with them. This paper presents an end-to-end system for courtesy amount recognition starting from check images as input to recognizing amounts as a sequence of digits. The system is a hybrid system, combining rule-based modules as well as machine learning modules. For the amount recognition system, both segmentation-based and segmentation-free approaches were investigated and compared. We evaluated our system on the CENPARMI dataset of real bank checks in Arabic. We achieve 67.4% accuracy at the amount level and 87.15% accuracy at the digit level on the test set consisting of 626 check images. The results are presented with detailed analysis, and some possible future work is identified. This work can be used as a baseline to benchmark future research in Arabic check courtesy amount recognition.

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Section: A System For Arabic Check Courtesy Amount Recognitionmentioning

confidence: 99%

A Hybrid Rule-Based and Machine Learning System for Arabic Check Courtesy Amount Recognition

Ahmad

2023

Sensors

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“…. 이외에도 기계학습 알고리 즘과 전문가시스템의 조합 (Li & Park, 2009;Villena-román et al, 2011), 미분류 문헌의 활 용 (김판준, 이재윤, 2007;Torii et al, 2011) (김판준, 2006a;Isa et al, 2008;Joachims, 1998;Yang & Liu, 1999…”

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A Study on the Reclassification of Author Keywords for Automatic Assignment of Descriptors

Kim¹,

Lee²

2012

Journal of the Korean Society for information Management

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This study purported to investigate the possibility of automatic descriptor assignment using the reclassification of author keywords in domestic scholarly databases. In the first stage, we selected optimal classifiers and parameters for the reclassification by comparing the characteristics of machine learning classifiers. In the next stage, learning the author keywords that were assigned to the selected articles on readings, the author keywords were automatically added to another set of relevant articles. We examined whether the author keyword reclassifications had the effect of vocabulary control just as descriptors collocate the documents on the same topic. The results showed the author keyword reclassification had the capability of the automatic descriptor assignment.

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