Using features of local densities, statistics and HMM toolkit (HTK) for offline Arabic handwriting text recognition

Abstract This paper presents an analytical approach of an offline handwritten Arabic text recognition system. It is based on the Hidden Markov Models (HMM) Toolkit (HTK) without explicit segmentation. The first phase is preprocessing, where the data is introduced in the system after quality enhancements. Then, a set of characteristics (features of local densities and features statistics) are extracted by using the technique of sliding windows. Subsequently, the resulting feature vectors are injected to the Hidden Markov Model Toolkit (HTK). The simple database “Arabic-Numbers” and IFN/ENIT are used to evaluate the performance of this system.

• Publication year

  2017

• Venue

  Journal of Electrical Systems and Information Technology

• Publication date

  2017-12-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1016/J.JESIT.2016.07.005
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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