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A. Amin
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ABSTRACT: Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking, business and data entry applications. The main theme of this paper is the automatic recognition of hand-printed Arabic characters using machine learning. Conventional methods rely mainly on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over a large degree of variation between writing styles, and recognition rules can be constructed by example. The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the correct average recognition rate obtained using cross-validation was 87.23%.
Pattern Recognition, 2002. Proceedings. 16th International Conference on; 02/2002
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A. Amin
[show abstract]
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ABSTRACT: Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking, business and data entry applications. The main theme of this paper is the automatic recognition of hand-printed Arabic characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over the large degree of variation between writing styles and recognition rules can be constructed by example. The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the correct average recognition rate obtained using cross-validation was 86.65%
Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on; 02/2001
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ABSTRACT: Document image processing has become an increasingly important technology in the automation of office documentation tasks.
Automatic document scanners such as text readers and OCR (Optical Character Recognition) systems are an essential component
of systems capable of those tasks. One of the problems in this field is that the document to be read is not always placed
correctly on a flatbed scanner. This means that the document may be skewed on the scanner bed, resulting in a skewed image.
This skew has a detrimental effect on document on document analysis, document understanding, and character segmentation and
recognition. Consequently, detecting the skew of a document image and correcting it are important issues in realising a practical
document reader. In this paper we describe a new algorithm for skew detection. We then compare the performance and results
of this skew detection algorithm to other publidhed methods form O'Gorman, Hinds, Le, Baird, Posel and Akuyama. Finally, we
discuss the theory of skew detection and the different apporaches taken to solve the problem of skew in documents. The skew
correction algorithm we propose has been shown to be extremenly fast, with run times averaging under 0.25 CPU seconds to calculate
the angle on the DEC 5000/20 workstation.
Formal Pattern Analysis & Applications 08/2000; 3(3):243-253. · 0.74 Impact Factor
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A. Amin
[show abstract]
[hide abstract]
ABSTRACT: Character recognition systems can contribute tremendously to the
advancement of the automation process and can improve the interaction
between man and machine in many applications, including office
automation, check verification and a large variety of banking, business
and data entry applications. The main theme of the paper is the
automatic recognition of hand-printed Arabic characters using machine
learning. Conventional methods have relied on hand-constructed
dictionaries which are tedious to construct and difficult to make
tolerant to variation in writing styles. The advantages of machine
learning are that it can generalize over the large degree of variation
between writing styles and recognition rules can be constructed by
example. The system was tested on a sample of handwritten characters
from several individuals whose writing ranged from acceptable to poor in
quality and the correct average recognition rate obtained using
cross-validation was 89.65%
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 03/2000; · 2.01 Impact Factor
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ABSTRACT: The goal of character recognition research is to simplify and
automate the development of character recognition algorithms. We
describe an approach based on applying preprocessing to data sets of
Latin characters and then applying a machine learning approach to the
data sets to build a knowledge base able to classify unseen
pre-processed characters. The machine learning method, Induct/RDR, has a
number of features that make it particularly suitable for character
recognition. It has the potential to integrate automatic analysis with a
manual knowledge acquisition methodology if further refinement is
required. Initial results on hand-printed Latin characters show the
recognition accuracy of up to 90.2% on unseen cases for the machine
learning system
Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on; 10/1999
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ABSTRACT: English and Chinese are languages which have attracted tremendous
interest from character recognition researchers. In contrast, research
in the field of character recognition for Arabic/Persian scripts faces
major problems, mainly related to their unique characteristics, like
being cursive, the multiple shapes of one character in different
positions in a word, and the connectivity of characters on the baseline.
The work proposed in this paper consists of three major phases. After
digitizing the text, the original image is transformed into a gray-scale
image using a 300-dpi scanner. Different pre-processing steps are then
applied to the image file. In the next phase, sub-words of all words are
recognized and global features for each word are extracted. Contour
tracing plays a very important role in the feature extraction
phase
Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on; 10/1999
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ABSTRACT: This paper presents a new method for the recognition of Arabic
text using global features and fuzzy ARTMAP neural network. The method
is divided into three major steps. The first step is digitization and
pre-processing to create connected component. The second step is
concerned with feature extraction, where global features of the input
word are used to extract features such as number of subwords, number of
peaks within the subword, number and position of the complementary
character, etc., to avoid the difficulty of segmentation stage. The
third step is the classification and is composed of a single fuzzy
ARTMAP. The method was evaluated with 3255 images of 217 Arabic words
with different fonts (each word has 15 samples), and the mean correct
classification rate was 95.25%
Neural Networks, 1999. IJCNN '99. International Joint Conference on; 02/1999
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Neural Computing and Applications 01/1999; 8(1):67-76. · 0.70 Impact Factor
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Proceedings of International Joint Conference on Neural Networks IJCNN'99; 01/1999
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Pattern Anal. Appl. 01/1998; 1:130-141.
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ABSTRACT: Recognition of Chinese characters has been an area of great interest for many years, and a large number of research papers and reports have already been published in this area. There are several major problems with Chinese character recognition: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to generate. This paper presents a new technique for the recognition of hand-printed Chinese characters using machine learning C4.5. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The paper also discusses Chinese character recognition using dominant point feature extraction and C4.5. The system was tested with 900 characters (each character has 40 samples) and the rate of recognition obtained was 84%
Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on; 09/1997
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[show abstract]
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ABSTRACT: The main theme of the paper is the automatic segmentation of
Arabic words using mathematical morphology tools. The proposed algorithm
has been tested with a set of Arabic words written by different writers,
ranging from poor to acceptable quality. The initial experimental
results are very encouraging and promising
Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on; 09/1997
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ABSTRACT: The main theme of the paper is the automatic recognition of Arabic printed text using artificial neural networks in addition to conventional techniques. This approach has a number of advantages: it combines rule based (structural) and classification tests; feature extraction is inexpensive; and execution time is independent of character font and size. The technique can be divided into three major steps: The first step is preprocessing in which the original image is transformed into a binary image utilizing a 300 dpi scanner and then forming the connected component. Second, global features of the input Arabic word are then extracted such as number of subwords, number of peaks within the subword, number and position of the complementary character, etc. Finally, an artificial neural network is used for character classification. The algorithm was implemented on a powerful MS-DOS microcomputer and written in C
Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on; 09/1997
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A. Amin
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ABSTRACT: Machine simulation of human reading has been the subject of intensive research for almost three decades. A large number of research papers and reports have already been published on Latin, Chinese and Japanese characters. However, little work has been conducted on the automatic recognition of Arabic characters because of the complexity of printed and handwritten text, and this problem is still an open research field. The main objective of this paper is to present the state of Arabic character recognition research throughout the last two decades
Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on; 09/1997
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ABSTRACT: This paper presents a new approach to building classifiers for
Chinese character recognition. A new knowledge acquisition technique is
used for building incrementally a knowledge based system for the
classification task. The new knowledge acquisition technique is based on
ripple down rules, which is an effective method for building large
knowledge bases. We extended ripple down rules to be able to acquire
graphical knowledge. In this paper a Hough transform technique is used
for extracting the features which are being used for a knowledge based
system. A prototype has been implemented and initial experimental
results are promising
Pattern Recognition, 1996., Proceedings of the 13th International Conference on; 09/1996
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ABSTRACT: We present a fully automated process to scan the Australian Telecom Yellow Pages and produce a text document consisting only of the business entries, while removing the advertisements, graphics and notes about the Yellow Pages. The system contains four major components: digitisation and thresholding, skew detection, segmentation (removal of unwanted parts of the image), and finally the recognition engine utilising the principles of mathematical morphology. This paper presents the current research, which consists of the process described above up to image segmentation. All the algorithms are written in C on a 5000/20 DEC workstation. We have tested more than 30 images with extremely promising results
Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on; 09/1995
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[show abstract]
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ABSTRACT: This paper presents a new technique for the recognition of hand-printed Latin characters using machine learning. Conventional methods have relied on manually constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalise over a large degree of variation between writing styles and recognition rules can be constructed by example. Characters are scanned into the computer and preprocessing techniques transform the bit-map representation of the characters into set of primitives which can be represented in an attribute base form. A set of such representations for each character is then input to C4.5 which produces a decision tree for classifying each character
Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on; 09/1995
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ABSTRACT: This paper presents the use of analysing the connected components extracted from the binary image of a document page. Such an analysis provides a lot of useful information, and will be used to perform skew correction, segmentation and classification of the document. We present a new algorithm for determining the skew angle of lines of text in an image of a document with the advantage that it only performs one iteration to determine the skew angle. Experiments on over 30 pages show that the method works well on a wide variety of layouts, including sparse textual regions, mixed fonts, multiple columns, and even for documents with a high graphical content
Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on; 09/1995
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[show abstract]
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ABSTRACT: The paper proposes a structural technique for automatic
recognition of hand printed Arabic characters. The advantages of this
technique are: more efficient for large and complex sets such as Arabic
characters; not expensive for feature extraction; and its execution time
does not depend on either the font or the size of the characters. The
algorithm was implemented on a microcomputer and tested by 10 different
users. The recognition rate obtained was about 90%
Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on; 11/1994
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Yehia El Mahgary,
A.-F. Ibrahim,
M.A.-F. Shama,
A. Hassan,
M.A.-H. Rifai,
M. Selim,
I. Abdel Gelil,
H. Korkor,
Anhar Higazi, A. Amin,
F. Bedewi,
Juha Forsström
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ABSTRACT: Within the frame of UNEP's project on the Methodologies of Determining the Costs of Abatement of GHG emissions, a case study on Egypt was undertaken by the Technical Research Centre of Finland (VTT) in cooperation with the Egyptian Environment Affairs Authority (EEAA), together with an expert team from different Egyptian organizations. Both bottom-up and top-down approaches were used. Several measures/technologies, including energy conservation, fuel switching, use of renewable energy and material replacement, were considered to decrease CO2 emissions. It was found that most of the measures were cost-effective, as a considerable potential for energy conservation exists in Egypt. The impact of energy conservation measures on the economy of the country was found to be positive using a macroeconomic model
Energy Policy. 02/1994;
