Literal Amounts Research Articles

Arabic literal amount sub-word recognition using multiple features and classifiers

Bank check processing is an important application of document analysis and recognition. Recognising the literal amounts from the check images is challenging and an open research problem. In this paper, we present our work on Arabic bank check literal amounts' sub-word recognition using four sets of features and three classifiers namely: support vector machine (SVM), neural network (NN), and decision tree forest (DTF) classifiers. In addition, we investigated two different approaches for classifier fusion. We tested our system on the CENPARMI database of Arabic bank check images. Our recognition results outperform previous published results on the same database.

Arabic literal amount sub-word recognition using multiple features and classifiers

Arabic literal amount sub-word recognition using multiple features and classifiers

New Segmentation Method for Analytical Recognition of Arabic Handwriting Using a Neural-Markovian Method

A new hybrid system of off-line analytical recognition of Arabic handwriting combining a neural network type multi-layer perceptron (MLP) and hidden Markov models (HMM) is presented. We propose a way to cooperate HMM and MLP neural network in a probabilistic architecture taking advantage of both tools dedicated to the recognition of Arabic literal amounts. This description is based on statistical and structural characteristics extraction of the significant character of the handwritten Arabic words, which can be used in the MLP classification module to estimate probabilities used as the observations to perform a recognition by the HMM. The originality of our approach is based on the segmentation into characters taking into account diacritics with the characters that match them. The experiments show the convergence of the global system, even with a random initialization of the neural network. Keywords - Recognition of Arabic handwriting, hidden Markov models, fast K-means, Arabic literal amounts, multi-layer perceptron. * E-mail: khaoula_1190@hotmail.com

New Segmentation Method for Analytical Recognition of Arabic Handwriting Using a Neural-Markovian Method

A new hybrid system of off-line analytical recognition of Arabic handwriting combining a neural network type multi-layer perceptron (MLP) and hidden Markov models (HMM) is presented. We propose a way to cooperate HMM and MLP neural network in a probabilistic architecture taking advantage of both tools dedicated to the recognition of Arabic literal amounts. This description is based on statistical and structural characteristics extraction of the significant character of the handwritten Arabic words, which can be used in the MLP classification module to estimate probabilities used as the observations to perform a recognition by the HMM. The originality of our approach is based on the segmentation into characters taking into account diacritics with the characters that match them. The experiments show the convergence of the global system, even with a random initialization of the neural network.Keywords - Recognition of Arabic handwriting, hidden Markov models, fast K-means, Arabic literal amounts, multi-layer perceptron. * E-mail: khaoula_1190@hotmail.com

Multiclassifiers system for handwritten Arabic literal amounts recognition based on enhanced feature extraction model

This paper proposes a recognition system for handling Arabic literal amounts based on three proposed strategies. First, an enhanced feature extraction model based on the part of Arabic word (PAW) analysis is introduced. It combines both the statistical features extracted through the word and the structural ones extracted through its PAWs. Second, the existing lexicon used in literal amounts is first partitioned into four subgroups according to the number of PAWs of the candidate word. Each subgroup is independently learned by a corresponding set of support vector machines (SVMs). However, several candidate words may contain some touching PAWs. For this reason, a proposed preclassification step is included and the classification of each candidate word is simultaneously done with and without correction of the touching PAWs. In both cases, the candidate sample is presented to the corresponding set of SVMs, where the two classification scores are computed. Then, the final decision is given according to the better score. Experimental results based on the Arabic handwritten database dataset confirm the effectiveness and robustness of the proposed recognition system in terms of classification accuracy, which reaches 95.91%.

Enhanced structural perceptual feature extraction model for Arabic literal amount recognition

One of the important applications for document recognition is the bank cheque processing, which is known as cheque literal amount. A few studies focused on Arabic bank cheque processing system compared to other systems, such as Latin and Chinese. The Arabic script has a number of characteristics that makes it unique among other scripts. It is known that humans are the best pattern recognisers. As such, the features detected while human reads the script can get better recognition rates. Therefore, proposing human reading inspired features which are called perceptual features can overcome the unique technical challenges in Arabic literal amount recognition. In this paper, the enhanced structural perceptual feature extraction model PFM has been proposed. Two main groups of features, which are the components and dots features and the loops and characters shapes features were combined to construct the PFM. This model was evaluated on standard Arabic Handwriting DataBase AHDB dataset. The PFM results outperformed the results reported in the previous studies.

Recognition of Handwritten Arabic Literal Amounts Using a Hybrid Approach

This paper describes a new approach to combine a multilayer perceptron (MLP) and a hidden Markov model for recognizing handwritten Arabic words. As a first step, connected components (CCs) of black pixels are detected, then the system determines which CCs are sub-words and which are diacritics. The diacritics are then isolated and identified separately, and the sub-words are segmented into graphemes. The MLP is used as labeller (classifier) and probability estimator. We also introduce the diacritics and their positions in our hybrid system; thus, only one model including both grapheme and diacritic states is built to represent the whole alphabet. Finally, we consider a maximum likelihood classifier to decide about the word class. The experiments that were performed show promising results on Arabic word segmentation and recognition.

A Hybrid Neuro-Symbolic Approach for Arabic Handwritten Word Recognition

In this article, we suggest a system that automatically constructs knowledge based artificial neural networks (KBANN) for the holistic recognition of handwritten Arabic words in limited lexicons. To build a neuro-symbolic KBANN classifier for a given vocabulary, ideal samples of its words are first submitted to a structural feature extraction module. The analysis of the presence and possible occurrence numbers for these features in the considered lexicon enables to generate a symbolic knowledge base reflecting a hierarchical classification of the words. A rules-to-network translation algorithm uses this knowledge to build a multilayer neural network. It determines precisely its architecture and initializes its connections with specific values rather than random values, as is the case in classical neural networks. This construction approach provides the network with theoretical knowledge and reduces the training stage, which remains necessary because of styles and writing conditions variability. After this empirical training stage using real examples, the network acquires a final topology, which allows it to recognize new handwritten words. The proposed method has been tested on the automated construction of neuro-symbolic classifiers for two Arabic lexicons: literal amounts and city names. The application of this approach to the recognition of handwritten words or characters in different scripts and languages is also considered.

Classifiers combination and syntax analysis for Arabic literal amount recognition

Automatic handwriting recognition has a variety of applications in real world problems, such as mail sorting and check processing. Recently, it has been demonstrated that combining the decisions of several classifiers and integrating multiple information sources can lead to better recognition results. This article presents an approach for recognizing handwritten Arabic literal (legal) amounts. The proposed system uses a set of holistic structural features to describe the words. These features are presented to three classifiers: multilayer neural network, k nearest neighbor, and fuzzy k nearest neighbor. The classification results are then combined using several schemes; we retained the score summation one for this work. A syntactic post-classification process is then carried out to find the best match among the candidate words. The performance of this approach is superior to the system which ignores all contextual information and simply relies on the recognition scores of the recognizers.

Arabic Word Recognition by Classifiers and Context

Given the number and variety of methods used for handwriting recognition, it has been shown that there is no single method that can be called the "best". In recent years, the combination of different classifiers and the use of contextual information .have become major areas of interest in improving recognition results. This paper addresses a case study on the combination of multiple classifiers and the integration of syntactic level information for the recognition of handwritten Arabic literal amounts. To the best of our knowledge, this is the first time either of these methods has been applied to Arabic word recognition. Using three individual classifiers with high level global features, we performed word recognition experiments. A parallel combination method was tested for all possible configuration cases of the three chosen classifiers. A syntactic analyzer makes a final decision on the candidate words generated by the best configuration scheme. The effectiveness of contextual knowledge integration in our application is confirmed by the obtained results.

Multi-Bank Check Recognition System: Consideration on the Numeral Amount Recognition Module

This paper presents a complete numeral amount recognition module which is integrated in an automatic system aimed at reading all types of French checks. This module is combined with an automatic reading system of literal amounts. This complete working system, called LIREChèques, is developed by MATRA MS&I and is now in advanced test at SERINTEL, a pilot site. Two aspects of the numeral amount recognition system are particularly emphasized: the numeral recognition stage itself and the syntactic analysis stage. The numeral recognition module relies on a combination of two individual classifiers, the first one is based on concavity measurements, the second one on both statistical and structural features. The syntactic analysis, called syntactic/contextual analysis, is combined with contextual information to take into account the segmentation behaviour and the presence of literal entities in the numeral amount. We demonstrate that very good performances can be obtained on digits such as those extracted from numeral amounts since a substitution rate of 0.06% while still preserving a recognition rate of near 87% can be achieved. As for the syntactic/contextual analysis stage, results obtained on a test set (containing checks from more than 40 different banks and 15/ of typed checks, thus being a good representation of the real tests realized on site) show clearly that introduction of contextual information in association with syntactic analysis allows to process much more numeral amounts than a simple syntactic analysis and increases perceptibility of the recognition rate.

Automatic reading of the literal amount of bank checks

The studies presented in this paper deal with the global recognition of a restricted variety of handwritten words comprising the vocabulary used to write French bank checks. Several tools have been developed within the constraints of this application, tools that relate to the general problem of off-line cursive handwriting recognition. The first difficulty when one wants to read a text is the location of words. This is done by using a top-down analysis that first locates lines of text before segmenting them into individual words. A structural model of cursive handwriting, built on the median axis of the word, is proposed. This constitutes an alternative to the use of the conventional, letter-by-letter analytical model and could be used for other problems involving cursive word recognition. According to this structural model of cursive handwriting, an edit distance is computed between the extracted structural description and the reference descriptions that are interpreted as grapheme strings. This provides an ordered list of candidates for each individual word. Feature extraction in the binary image of the word is performed using a specific line-following algorithm. Since it is possible to express the syntax of the sentences by a finite grammar, this information is used to discard the inconsistent sentences from the possible ones. These various algorithms have been tested on personal data, as well as on real check images.