A Hybrid NN/HMM modeling technique for online Arabic handwriting recognition (original) (raw)
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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.
Contextual Arabic Handwriting Recognition System using Embedded Training based Hybrid HMM/MLP Models
Transactions on Machine Learning and Artificial Intelligence, 2017
Recognizing unconstrained cursive Arabic handwritten text is a very challenging task the use of hybrid classification to take advantage of the strong modeling of Hidden Markov Models (HMM) and the large capacity of discrimination related to Multilayer Perceptron (MLP) is a very important component in recognition systems.The proposed work reports an effective method on improvement our previous work that takes into consideration the context of character by applying an embedded training based HMMs this HMM is enhanced by anArtificial neural network that are incorporated into the process of classification to estimate the emission probabilities. The experiments are done on the same benchmark IFN/ENIT database of our previous work to compare the results and show the effectiveness of hybrid classifier for enhancing the recognition rate the results are promising and encouraging.
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.
Journal of Data Mining and Digital Humanities, 2016
In this paper we present a system for offline recognition cursive Arabic handwritten text which is analytical without explicit segmentation based onHidden Markov Models (HMMs). Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models. The HMM-based classifiercontains a training module and a recognition module. The training module estimates theparameters of each of the character HMMs uses the Baum-Welchalgorithm. In the recognition phase, feature vectors extracted from an image are passed to a network of word lexicon entries formed of character models. The character sequence providing the maximumlikelihood identifies the recognized entry. If required, the recognition can generate N best output hypotheses rather than just the single best one. To determine the best outputhypotheses, the Viterbi algorithm is used.The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.
Off-lexicon online Arabic handwriting recognition using neural network
This paper highlights a new method for online Arabic handwriting recognition based on graphemes segmentation. The main contribution of our work is to explore the utility of Beta-elliptic model in segmentation and features extraction for online handwriting recognition. Indeed, our method consists in decomposing the input signal into continuous part called graphemes based on Beta-Elliptical model, and classify them according to their position in the pseudo-word. The segmented graphemes are then described by the combination of geometric features and trajectory shape modeling. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results using the benchmarking ADAB Database show the performance of the proposed method.
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.
A Time Delay Neural Network for Online Arabic Handwriting Recognition
Advances in Intelligent Systems and Computing, 2017
Handwriting recognition is an interesting part in pattern recognition field. In the last decade, several approaches are focused on online handwriting recognition because the very rapid growth of new technologies in the field of data entry. In this paper, we propose a new system for online Arabic handwriting recognition based on beta-elliptic model which allow to segment the trajectory into segments called strokes by inspecting the extremums points of velocity profile and extract their dynamic and geometric profiles. These strokes are used to train the Time Delay Neural Network (TDNN) which is able to represent the sequential aspect of input data. To evaluate our method, we have used a total of 25000 Arabic letters from the LMCA database. Our experimental results demonstrate the effectiveness of our proposed method and show recognition rates exceeds the 95%. Keywords: Time delay neural network Á Beta-elliptic model Á Beta impulse Á Velocity Á Receptive fields Á Shared weights
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.
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.