RETRACTED ARTICLE: An efficient recognition system for preserving ancient historical documents of English characters

The clusters of historical documents are of great importance in terms of cultural and scientific. In order to access the documents, originality should be maintained. So conversion of digital form is highly required for recognition. While converting, those documents may be due to poor quality, overla...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2021-06, Vol.12 (6), p.6275-6283
Main Authors: Sathya Narayanan, V., Kasthuri, N.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Character recognition
Computational Intelligence
Datasets
Decision trees
Documents
Engineering
Methods
Morphology
Neural networks
Original Research
Pattern recognition
Performance evaluation
Robotics and Automation
Support vector machines
User Interfaces and Human Computer Interaction
Visual impairment
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Summary:The clusters of historical documents are of great importance in terms of cultural and scientific. In order to access the documents, originality should be maintained. So conversion of digital form is highly required for recognition. While converting, those documents may be due to poor quality, overlapping of characters, complex background and so on. In this paper, an efficient system for recognizing English characters from degraded historical document images is proposed. Initially, modified Adaptive Thresholding based binarization process is performed to eliminate the noise content in the input image. The characters are segmented through the rectangular bounding box method. Then Local binary pattern (LBP) algorithm is enforced to extricate the features of each characters. Finally, Spatial Pyramid Matching (SPM) classifier is used for texture classification. HDLA 2011 dataset is employed to validate the proposed method. The proposed method achieves 94.6% recognition accuracy and 0.34 s computation time for Lucida Black-letter font. This method also outperforms better than the existing recognition techniques.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-020-02201-w