Abstract:
In this thesis, we address Arabic offline handwritten recognition for historical documents. The Automation of the handwritten recognition has many
applications, such as zip coding, forms processing, indexing and retrieving
historical manuscripts and so on. Recognition for Arabic handwritten script
lags far compared to other languages such as Latin, and Chinese texts. The
challenges for Arabic language raise from its nature such as overlapping characters, cursive texts, and lack of benchmark databases.
In this work, we introduced new techniques for different phases of the offline Arabic handwritten recognition. First it addresses the recognition of
the Arabic handwritten for historical manuscripts not contemporary scripts.
In addition, the feature selection algorithm is presented. This work aims
to select appropriate features and remove irrelevant ones. The relevant features are those which enhance the results and give a higher success rates.
We depend on probabilistic classifier not statistically such as HMM. Naive
Bayesian classifier is used for training and classification.
The presented work was applied and tested on private database, which was
collected from historical manuscripts. A competitive recognition rates were
achieved.
The results show that applying feature selection prior classification gives
haghier success rates than classification without feature selection
