2015 7th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) 2015
DOI: 10.1109/ecai.2015.7301192
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Adaptive clustering algorithm for optical character recognition

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Cited by 3 publications
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“…ough image binarization and segmentation are not new, it is very difficult to find a more generalized algorithm for this task due to numerous challenges such as image degradation types, artifacts, uneven illuminations, and inherent noise in the acquisition process. Each application of image binarization such as optical character recognition (OCR) [3,4], document binarization, image restoration, and many machine vision applications may present different sets of challenges [5]. For instance, in a task where segmentation may be utilized as a preprocessing stage, a segmentation approach with low computational cost may be desirable, and in some machine vision applications where hardware has low processing and memory capacity, some available methods may not be applicable [2].…”
Section: Introductionmentioning
confidence: 99%
“…ough image binarization and segmentation are not new, it is very difficult to find a more generalized algorithm for this task due to numerous challenges such as image degradation types, artifacts, uneven illuminations, and inherent noise in the acquisition process. Each application of image binarization such as optical character recognition (OCR) [3,4], document binarization, image restoration, and many machine vision applications may present different sets of challenges [5]. For instance, in a task where segmentation may be utilized as a preprocessing stage, a segmentation approach with low computational cost may be desirable, and in some machine vision applications where hardware has low processing and memory capacity, some available methods may not be applicable [2].…”
Section: Introductionmentioning
confidence: 99%
“…In OCR system feature extraction from overlapping character blocks is major problem and reduces the performance of the recognition phase. The authors (11) address this problem and proposed a system to escalation the performance of the recognition phase by the help of clustering system and Hamming Distance method and experimental show improvement in performance. In (12,13) proposed precise recognition system for recognition of Historical printed records by the combination of LSTM neural network and clustering and improve the recognition rate by combine the clustering and classification.…”
Section: Introductionmentioning
confidence: 99%