Printed Persian Subword Recognition Using Wavelet Packet Descriptors

Abstract

In this paper, we present a new approach to offline OCR (optical character recognition) for printed Persian subwords using wavelet packet transform. The proposed algorithm is used to extract font invariant and size invariant features from 87804 subwords of 4 fonts and 3 sizes. The feature vectors are compressed using PCA. The obtained feature vectors yield a pictorial dictionary for which an entry is the mean of each group that consists of the same subword with 4 fonts in 3 sizes. The sets of these features are congregated by combining them with the dot features for the recognition of printed Persian subwords. To evaluate the feature extraction results, this algorithm was tested on a set of 2000 subwords in printed Persian text documents. An encouraging recognition rate of 97.9% is got at subword level recognition.

Studi Banding Implementasi Metode Hidden Markov Model dalam Pengenalan Tulisan Tangan

“…Pengenalan tulisan tangan secara offline dapat berupa pengenalan teks tulisan tangan [2] maupun teks hasil ketikan [3]. Sedangkan pengenalan tulisan tangan secara online hanya dapat berupa teks tulisan tangan [4,5].…”

A Holistic Technique for an Arabic OCR System

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