Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Survey and bibliography of Arabic optical text recognition
Signal Processing
An Omnifont Open-Vocabulary OCR System for English and Arabic
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Role of Holistic Paradigms in Handwritten Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transcript Mapping for Historic Handwritten Document Images
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Retrieval of Ottoman documents
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Employee turnover: a novel prediction solution with effective feature selection
WSEAS Transactions on Information Science and Applications
Benchmark database and GUI environment for printed Arabic text recognition research
WSEAS Transactions on Information Science and Applications
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This paper presents a novel holistic technique for classifying Arabic handwritten text documents. The classification of Arabic handwritten documents is performed in several steps. First, the Arabic handwritten document images are segmented into words, and then each word is segmented into its connected parts. Second, several structural and statistical features are extracted from these connected parts and then combined to represent a word with one consolidated feature vector. Finally, a generalized feedforward neural network is used to learn and classify the different styles/fonts into word classes, which are used to retrieve Arabic handwritten text documents. The extraction of structural and statistical features from the individual connected parts as compared to the extraction of these features from the whole word improved the performance of the system.