On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm (original) (raw)
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On-line Arabic Handwriting Recognition System based on HMM
The purpose of this research is to improve the recognition rate of on-line Arabic handwriting recognition using HMM (Hidden Markov Model). Delayed strokes are removed from the on-line Arabic word to avoid the difficulty and the confusion caused by the delayed strokes in the recognition process. Dictionaries for all the words in the ADAB database have been constructed with and without the delayed strokes. Word matching in both dictionaries along with effective on-line features and careful choice of the HMM parameters have significantly improved the recognition rate of the proposed system over other HMM-based on-line Arabic handwriting recognition systems.
In this paper, we present a novel segmentation- free Arabic handwriting recognition system based on hidden Markov model (HMM). Two main contributions are intro- duced: a new technique for dividing the image into nonuni- form horizontal segments to extract the features and a new technique for solving the problems of the skewing of char- acters by fusing multiple HMMs. Moreover, two enhance- ments are introduced: the pre-processing method and feature extraction using concavity space. The proposed system first pre-processes the input image by setting the thickness of the input word to three pixels and fixing the spacing between the different parts of the word. The input image is divided into constant number of nonuniformhorizontal segments depend- ing on the distribution of the foreground pixels. A set of robust features representing the gradient of the foreground pixels is extracted using sliding windows. The input image is decomposed into several images representing the verti- cal, horizontal, left diagonal and right diagonal edges in the image. A set of robust features representing the densities of the foreground pixels in the various edge images is extracted using slidingwindows. The proposed systembuilds character HMM models and learns word HMM models using embed- ded training. Besides the vertical slidingwindow, two slanted sliding windows are used to extract the features. Three dif- ferent HMMs are used: one for the vertical sliding window and two for the slanted windows. A fusion scheme is used to combine the three HMMs. The proposed system is very promising and outperforms all the other Arabic handwriting recognition systems reported in the literature.
Combining Online and Offline Systems for Arabic Handwriting Recognition
The purpose of this research is to improve the recognition rate of online Arabic handwriting recognition using HMM (Hidden Markov Model). Delayed strokes are removed from the online Arabic word to avoid the difficulty and the confusion caused by the delayed strokes in the recognition process. A new technique for extracting offline features by dividing the image into non-uniform horizontal segments is presented. The integration between online and offline approaches has proven to give a better performance. With the combination we could increase the system performance over the best individual recognizer by 2.38%.
Online Arabic Handwriting Recognition Using Hidden Markov Models
Online handwriting recognition of Arabic script is a difficult problem since it is naturally both cursive and unconstrained. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. This paper introduces a Hidden Markov Model (HMM) based system to provide solutions for most of the difficulties inherent in recognizing Arabic script including: letter connectivity, position-dependent letter shaping, and delayed strokes. This is the first HMM-based solution to online Arabic handwriting recognition. We report successful results for writerdependent and writer-independent word recognition.
The purpose of this research is to improve the recognition rate of on-line Arabic handwriting recognition using HMM (Hidden Markov Model). Delayed strokes are removed from the on-line Arabic word to avoid the difficulty and the confusion caused by the delayed strokes in the recognition process. Dictionaries for all the words in the ADAB database have been constructed with and without the delayed strokes. Word matching in both dictionaries along with effective on-line features and careful choice of the HMM parameters have significantly improved the recognition rate of the proposed system over other HMM-based on-line Arabic handwriting recognition systems.
Effective Technique for the Recognition of Writer Independent Off-line Handwritten Arabic Words
In this paper we present a novel segmentation-free Arabic handwriting recognition system based on hidden Markov model (HMM). Two main contributions are introduced: a novel pre-processing method and a new technique for dividing the image into non uniform horizontal segments to extract the features. The proposed system first pre-processes the input image by setting the thickness of the input word to three pixels and fixing the spacing between the different parts of the word. The input image is then divided into constant number of non uniform horizontal segments depending on the distribution of the foreground pixels. A set of robust features representing the foreground pixels is extracted using vertical sliding windows. The proposed system builds character HMM models and learns word HMM models using embedded training data. The performance of the proposed system is very promising compared with other Arabic handwriting recognition systems available in the literature.
Off-Line Arabic Handwriting Recognition system based on concavity features and HMM classifier
In this paper we present a novel segmentation-free Arabic handwriting recognition system based on hidden Markov model (HMM). The proposed system first pre-processes the input image. The input image is then decomposed into several images representing the vertical, horizontal, left diagonal and right diagonal edges in the image. A set of robust features representing the densities of the foreground pixels in the various edge images is extracted using sliding windows. The proposed system builds character HMM models and learns word HMM models using embedded training. The performance of the proposed system is very promising compared with other Arabic handwriting recognition systems available in the literature. The contribution of this work is the novel feature extraction technique.
Improvements in BBN's HMM-Based Offline Arabic Handwriting Recognition System
2009
Offline handwriting recognition of free-flowing Arabic text is a challenging task due to the plethora of factors that contribute to the variability in the data. In this paper, we address some of these sources of variability, and present experimental results on a large corpus of handwritten documents. Specific techniques such as the application of context-dependent Hidden Markov Models (HMMs) for the cursive Arabic script, unsupervised adaptation to account for the stylistic variations across scribes, and image pre-processing to remove ruled-lines are explored. In particular, we proposed a novel integration of structural features in the HMM framework which exclusively results in a 9% relative improvement in performance. Overall, we demonstrate a relative reduction of 17% in word error rate over our baseline Arabic handwriting recognition system.
Recognition of Off-Line Handwritten Arabic Words Using Hidden Markov Model Approach
2002
The main steps of document processing have been reviewed, especially those implemented on Arabic writing. The techniques used in this research, such as Vector Quantization (VQ), Hidden Markov Models (HMM), and Induction of Decision Trees (ID3) have been considered, as well as reviewing pre-processing and feature extraction used in Arabic writing.
2009 10th International Conference on Document Analysis and Recognition, 2009
This paper presents an off-line Arabic Handwriting recognition system based on the selection of different state of the art features and the combination of multiple Hidden Markov Models classifiers. Beside the classical use of the off-line features, we add the use of on-line features and the combination of the developed systems. The designed recognizer is implemented using the HMM-Toolkit. In a first step, we use different features to make the classification and we compare the performance of single classifiers. In a second step, we proceed to the combination of the on-line and the off-line based systems using different combination methods. The system is evaluated using the IFN/ENIT database. The recognition rate is in maximum 63.90% for the individual systems. The combination of the on-line and the off-line systems allows to improve the system accuracy to 81.93% which exceeds the best result of the ICDAR 2005 competition.