The IFN/ENIT-database contains material for training and testing of Arabic handwriting recognition software. There are more than 2200 binary images of handwriting sample forms from 411 writers, about 26,000 binary word images have been isolated from the forms and saved individually for ease of access. A ground truth file for each word in the database has been compiled. This file contains information about the word such as the position of the words base line, and information on the individual used characters in the word.
Published in Chapter:
A Metaheuristic Algorithm for OCR Baseline Detection of Arabic Languages
F. Daneshfar (University of Kurdistan, Iran), W. Fathy (University of Kurdistan, Iran), and B. Alaqeband (University of Kurdistan, Iran)
Copyright: © 2015
|Pages: 28
DOI: 10.4018/978-1-4666-7258-1.ch023
Abstract
Preprocessing is a very important part of cursive languages Optical Character Recognition (OCR) systems. Thus, baseline detection, which is one of the main parts of the preprocessing operation, plays a basic role on OCR systems; improvement on baseline detection could be absolutely useful for decreasing errors in recognition words. In this chapter, a metaheuristic- and mathematical-based algorithm is recommended, which has improved the baseline detection process in relation to the well-known baseline detection algorithms. The most important advantages of the proposed method are simplicity, high speed processing, and reliability. To test this novel solution, IFN/ENIT database, which is a well-known and attending database, is utilized. However, the proposed solution is reliable to any standard database of cursive language's OCR.