Maher Ahmed

A rotation invariant rule-based thinning algorithm for character recognition

This paper presents a novel rule-based system for thinning. The unique feature that distinguishes our thinning system is that it thins symbols to their central lines. This means that the shape of the symbol is preserved. It also means that the method is rotation invariant. The system has 20 rules in its inference engine. These rules are applied simultaneously to each pixel in the image. Therefore, the system has the advantages of symmetrical thinning and speed. The results show that the system is very efficient in preserving the topology of symbols and letters written in any language.

Wavelet approximation-based affine invariant shape representation functions

In this paper, new wavelet-based affine invariant functions for shape representation are presented. Unlike the previous representation functions, only the approximation coefficients are used to obtain the proposed functions. One of the derived functions is computed by applying a single wavelet transform; the other function is calculated by applying two different wavelet transforms with two different wavelet families. One drawback of the previously derived detail-based invariant representation functions is that they are sensitive to noise at the finer scale levels, which limits the number of scale levels that can be used. The experimental results in this paper demonstrate that the proposed functions are more stable and less sensitive to noise than the detail-based functions.

A new comprehensive database of handwritten Arabic words, numbers, and signatures used for OCR testing

This paper describes the formation of a comprehensive database of handwritten Arabic words, numbers, and signature, for use in optical character recognition research related to the Arabic language. So far no such (freely or commercially available) database exists.

Shape representation and description using the Hilbert curve

In this paper, a novel linear-time approach to shape representation and description is presented. The object shape is captured by scanning the object image using a space-filling curve (SFC). The resulting vector is smoothed, using wavelet approximation, and sampled. In addition, the concept of key feature points (KFPs) is introduced to utilize a priori information about the classification of the images in the database in optimizing the representation of the objects within each class. The proposed technique achieves a recognition rate of 88.3% on the MPEG-7 core experiment part B. On the Kimia-99 and Kimia-216 datasets, a precision average of 95.6% is attained. Retrieval rates of 94.2% and 95.6% are achieved on the gray-scale and binary versions of the ETH-80 dataset, respectively.

Coarse-to-fine multiscale affine invariant shape matching and classification

A multiscale algorithm for matching and classifying 2-D shapes is developed. The algorithm uses the 1-D dyadic wavelet transform (DWT) to decompose a shapes boundary into multiscale levels. Then the coarse to fine matching and classification are achieved in two stages. In the first stage, the global features are extracted by calculating the curve moment invariants of the approximation coefficients. By calculating the normalized cross correlation of the 1-D triangle area representation of the detail coefficients, the local similarity is achieved by the second stage. The proposed algorithm is invariant to the affine transformation and to the boundary starting point variation. In addition, the results demonstrate that the new algorithm is not sensitive to small boundary deformations.

An expert system for general symbol recognition

Abstract An expert system for analysis and recognition of general symbols is introduced. The system uses the structural pattern recognition technique for modeling symbols by a set of straight lines referred to as segments. The system rotates, scales and thins the symbol, then extracts the symbol strokes. Each stroke is transferred into segments (straight lines). The system is shown to be able to map similar styles of the symbol to the same representation. When the system had some stored models for each symbol (an average of 97 models/symbol), the rejection rate was 16.1% and the recognition rate was 83.9% of which 95% was recognized correctly. The system is tested by 5726 handwritten characters from the Center of Excellence for Document Analysis and Recognition (CEDAR) database. The system is capable of learning new symbols by simply adding their models to the system knowledge base.

Robust multiscale triangle-area representation for 2D shapes

In this paper, a new 2D shape multiscale triangle-area representation (MTAR) is proposed. This representation utilizes a simple geometric principle, the area of a triangle, in obtaining a robust and efficient shape representation. The use of the wavelet transform for decomposing the boundary of the shapes improves the efficiency and robustness of the representation. The MTAR is more robust to the affine transformation, less affected by noise, and more selective than similar methods, e.g., the curvature scale-space CSS. Two tests, using MPEG-7 CE-shape-1 database, show that MTAR achieves better performance than the CSS under affine transformation and in the general shape retrieval.

MTAR: A ROBUST 2D SHAPE REPRESENTATION

In this paper, a new 2D shape Multiscale Triangle-Area Representation (MTAR) method is proposed. This representation utilizes a simple geometric principle, that is, the area of the triangles formed by the shape boundary points. The wavelet transform is used for smoothing and decomposing the shape boundaries into multiscale levels. At each scale level, a TAR image and the corresponding Maxima-Minima lines are obtained. The resulting MTAR is more robust to noise, less complex, and more selective than similar methods such as the curvature scale-space (CSS). Furthermore, the MTAR is invariant to the general affine transformations. The proposed MTAR is tested and compared to the CSS method using MPEG-7 CE-shape-1 part B and Columbia Object Image Library (COIL-20) datasets. The results show that the proposed MTAR outperforms the CSS method for the conducted tests.

Affine invariant multiscale wavelet-based shape matching algorithm

In this paper, a multiscale wavelet-based algorithm for matching stand-alone shapes is developed. The algorithm uses the Dyadic Wavelet Transform (DWT) to decompose a shape¿s boundary into multi-scale levels. Features are extracted by calculating the curve moment invariants of the approximation coefficients. If the measured dissimilarity is small, then the shapes are globally similar. Local similarity is investigated by calculating the normalized cross correlation of the 1-D triangle area representation of the detail coefficients. The presented algorithm not only finds similar shapes, but it also can easily distinguish between seemingly similar shapes. The algorithm is invariant to the affine transformation and to the starting point variation of the shape contour.

An Efficient Shape Representation and Description Technique

In this paper, we present a novel approach to shape representation and description based on the combination of the Hilbert space filling curve and wavelet analysis. Our objective is to capitalize on the localization-preserving nature of the Hilbert space filling curve and the approximation power of the wavelet transform. The object image is scanned using the Hilbert curve and the resulting vector is smoothed using the wavelet transform and sampled. The technique is O(N) for both representation and comparison. We present some experimental results on the MPEG-7 dataset, Kimia-99 dataset, ETH-80 dataset, and a logo dataset.