Open Access
Text Segmentation for Document Recognition.
Nicola Nobile,Ching Y. Suen +1 more
- pp 257-290
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The article was published on 2014-01-01 and is currently open access. It has received 5 citations till now. The article focuses on the topics: Segmentation-based object categorization & Text segmentation.read more
Citations
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Journal ArticleDOI
Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions
TL;DR: The Systematic Literature Review discovered that AI-based approaches have a strong potential to extract useful information from unstructured documents automatically, however, they face certain challenges in processing multiple layouts of the unstructuring documents.
Generic Text Recognition using Long Short-Term Memory Networks
TL;DR: The research in document recognition is extended, from modern Latin scripts to Old Latin, to Greek and to other ``under-privilaged'' scripts such as Devanagari and Urdu Nastaleeq, to address the challenge of OCR of historical documents.
Journal ArticleDOI
FETNet: Feature erasing and transferring network for scene text removal
TL;DR: Li et al. as discussed by the authors proposed a Feature Erasing and Transferring (FET) mechanism to reconfigure the encoded features for scene text removal, which achieved state-of-the-art performance.
Journal ArticleDOI
A low cost IoT-based Arabic license plate recognition model for smart parking systems
TL;DR: In this article , the authors used the canny edge detection method with various thresholds, contour detection, and masking techniques to locate the car edges and license plate and achieved 93% accuracy.
Posted Content
A View of Regularized Approaches for Image Segmentation.
TL;DR: A brief overview of regularized mathematical models for image segmentation, considering edge-based and region-based variational models, as well as statistical and machine learning approaches, can be found in this article.
References
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Book
Discriminant Analysis and Statistical Pattern Recognition
TL;DR: In this article, the authors provide a systematic account of the subject area, concentrating on the most recent advances in the field and discuss theoretical and practical issues in statistical image analysis, including regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule.
BookDOI
Discriminant Analysis and Statistical Pattern Recognition: McLachlan/Discriminant Analysis & Pattern Recog
Book
Handbook of Character Recognition and Document Image Analysis
Horst Bunke,Patrick S. P. Wang +1 more
TL;DR: Arabic character recognition, A. Amin automatic reading of braille documents, and Antonacopoulos techniques for improving OCR results.
Book
Character Recognition Systems: A Guide for Students and Practitioners
TL;DR: This chapter discusses the development of Character Recognition, Evolution and Development, and some of the techniques used to achieve this goal, including Bayes Decision Theory, as well as some new methods based onributed graph matching.
Journal ArticleDOI
Handwritten digit recognition: investigation of normalization and feature extraction techniques
TL;DR: A comparison of normalization functions shows that moment-based functions outperform the dimension-based ones and the aspect ratio mapping is influential and the comparison of feature vectors shows that the improved feature extraction strategies outperform their baseline counterparts.