Handwritten Arabic Words Research Articles

Pixel distribution-based features for offline Arabic handwritten word recognition

Handwritten word recognition is the ability of a computer to receive and interpret intelligible handwritten input. An important document recognition application is bank cheque processing. The Arabic bank cheque processing system has not been studied as much as Latin and Chinese systems. The domain of handwriting in the Arabic script presents unique technical challenges; proposing a model for feature extraction which combines multiple types of features most likely will help to improve the recognition rate. This work proposed a pixel distribution-based features model (PDM) for offline Arabic handwritten word recognition. Two combination levels were used: the first combines different features and the second combination was done by ensemble classifiers. The AHDB dataset was used, and the experimental results showed superior performance when combining multiple features and using multi classifiers.

Segmentation Accuracy for Offline Arabic Handwritten Recognition Based on Bounding Box Algorithm

Character segmentation plays an important role in the Arabic optical character recognition (OCR) system, because the letters incorrectly segmented perform to unrecognized character. Accuracy of character recognition depends mainly on the segmentation algorithm used. The domain of off-line handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different segmentation algorithms for off-line Arabic handwriting recognition have been proposed and applied to various types of word images. This paper provides modify segmentation algorithm based on bounding box to improve segmentation accuracy using two main stages: preprocessing stage and segmentation stage. In preprocessing stage, used a set of methods such as noise removal, binarization, skew correction, thinning and slant correction, which retains shape of the character. In segmentation stage, the modify bounding box algorithm is done. In this algorithm a distance analysis use on bounding boxes of two connected components (CCs): main (CCs), auxiliary (CCs). The modified algorithm is presented and taking place according to three cases. Cut points also determined using structural features for segmentation character. The modified bounding box algorithm has been successfully tested on 450 word images of Arabic handwritten words. The results were very promising, indicating the efficiency of the suggested approach.

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers—that we proposed earlier—improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction.

An Efficient Segmentation Algorithm for Handwritten Arabic Words

Segmentation is an essential step for many Arabic handwritten Text recognition systems. It has a direct impact on the efficiency of subsequent steps in the recognition process such as feature extraction and classification, which strongly affects overall recognition performance. This paper presents a novel algorithm for off-line handwritten Arabic word segmentation into sub-words. This approach is based on vertical projection and a novel algorithm taking into consideration the characteristics of Arabic script. The proposed method has been successfully tested on the IFN/ENIT database and the results show the effectiveness of our approach in segmenting and isolating sub-words efficiently.

Offline Arabic Handwriting Recognition System Based on the Combination of Multiple Semi-Continuous HMMs

In this paper, we present a multi-classifier approach for off-line handwritten Arabic word recognition system. The main objective of this paper is to develop a handwriting recognition system that can be learned and applied to different Arabic writing styles. We propose an approach that combines multiple classifiers based on semi-continuous Hidden Markov Model using different feature extraction methods. The following process consists of several phases: pre-processing, extraction of pertinent characteristics and modeling. The role of the pre-processing phase is to prepare the input image text, i.e, binarization, normalization, segmentation and skeletonization. The obtained images are then used to extract features using a mixture of geometrical and statistical characteristics, namely, the intensities of gray level of the pixel, the densities and the concavities of the pixels, the VH2D projections and the invariants Hu moments. The modeling phase is based on the Hidden Markov Model using the HTK tools for the training and the recognition phase. Each character is modeled by a semi-continuous HMMs, and each set of feature has its own HMM. To improve the performance of our Arabic handwriting recognition system, we propose to combine parallel methods of HMMs having the same architecture, but training phase using four different types of primitive vectors. Our system was evaluated using the ENIT/IFN the base Arabic data. Results Obtained show that the combination of four semi-continuous HMM classifiers gives a significant improvement of our off-line handwriting recognition system compared to results obtained when using individual classifiers.

A novel fuzzy approach for handwritten Arabic character recognition

The aim of our work is to present a new method based on structural characteristics and a fuzzy classifier for off-line recognition of handwritten Arabic characters in all their forms (beginning, end, middle and isolated). The proposed method can be integrated in any handwritten Arabic words recognition system based on an explicit segmentation process. First, three preprocessing operations are applied on character images: thinning, contour tracing and connected components detection. These operations extract structural characteristics used to divide the set of characters into five subsets. Next, features are extracted using invariant pseudo-Zernike moments. Classification was done using the Fuzzy ARTMAP neural network, which is very fast in training and supports incremental learning. Five Fuzzy ARTMAP neural networks were employed; each one is designed to recognize one subset of characters. The recognition process is achieved in two steps: in the first one, a clustering method affects characters to one of the five character subsets. In the second one, the pseudo-Zernike features are used by the appropriate Fuzzy ARTMAP classifier to identify the character. Training process and tests were performed on a set of character images manually extracted from the IFN/ENIT database. A height recognition rate was reported .

SIFT descriptors for Arabic handwriting recognition

In this paper, a new recognition system for off-line Arabic handwritten words is presented. The proposed system uses for the first time scale invariant feature transform SIFT descriptors to extract features from Arabic handwritten words. It is also based on keypoints matching for achieving classification. In our method, word's images are divided vertically into five frames; from each frame a set of keypoints using SIFT descriptors is extracted and compared to classes' models which includes the most discriminating keypoints extracted from words samples of the same class. Finally, the recognition process was achieved through a keypoints matching procedure, using the Euclidean distance. The paper presents also a new large Arabic handwritten word database. This database provides a new framework for benchmarking and gives a new freely available handwritten word dataset. Several tests have been performed using our new database and the well known IFN/ENIT database for comparison purposes. The reported results show the robustness and efficiency of the proposed approach.

A PGM-based System for Arabic HandwrittenWord Recognition

This paper describes a system for off-line recognition of handwritten Arabic words. It uses simple andeasily extractable features to construct feature vectors for words in the vocabulary. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders and diacritic points. The system is evolved based on horizontal and vertical Hidden Markov Models and Dynamic Bayesian Network. Our strategy consists of looking for various architectures and selecting those which provide the best recognition performance. Experiments on handwritten Arabic words from IFN/ENIT database and ancient manuscripts strongly support the feasibility of the proposed system. The recognition rates achieve 91.89% (IFN/ENIT) and 94.61% (ancient manuscripts).

A Framework for Arabic Handwritten Recognition Based on Segmentation

Automatic off-line Arabic handwriting recognition still faces a big challenges. Due to the cursive nature of the Arabic language, most of published works are based on recognition of a whole word without segmentation. This paper presents a new framework for the recognition of handwritten Arabic words based on segmentation. This framework involves two phases (training phase and testing phase). In the training phase, Arabic handwritten characters were trained to be recognized, while in the testing phase, words were segmented into characters for recognition. Classification is achieved in two steps (classification of the segmented characters and classification of the word). A dictionary is constructed and used to correct any errors occurring during the previous stages of the recognition process. This work has been tested with IFN/ENIT database and a comparison made against some existing methods and promising results have been obtained.

Recognizing handwritten Arabic words using grapheme segmentation and recurrent neural networks

The Arabic alphabet is used in around 27 languages, including Arabic, Persian, Kurdish, Urdu, and Jawi. Many researchers have developed systems for recognizing cursive handwritten Arabic words, using both holistic and segmentation-based approaches. This paper introduces a system that achieves high accuracy using efficient segmentation, feature extraction, and recurrent neural network (RNN). We describe a robust rule-based segmentation algorithm that uses special feature points identified in the word skeleton to segment the cursive words into graphemes. We show that careful selection from a wide range of features extracted during and after the segmentation stage produces a feature set that significantly reduces the label error. We demonstrate that using same RNN recognition engine, the segmentation approach with efficient feature extraction gives better results than a holistic approach that extracts features from raw pixels. We evaluated this segmentation approach against an improved version of the holistic system MDLSTM that won the ICDAR 2009 Arabic handwritten word recognition competition. On the IfN/ENIT database of handwritten Arabic words, the segmentation approach reduces the average label error by 18.5 %, the sequence error by 22.3 %, and the execution time by 31 %, relative to MDLSTM. This approach also has the best published accuracies on two IfN/ENIT test sets.

Recognition of the Arabic Handwritten Words of the Algerian Departments

Recognition of the Arabic Handwritten Words of the Algerian Departments

FPGA Parallel Recognition Engine for Handwritten Arabic Words

Arabic optical character recognition (OCR) has been an active field of research for decades. Yet, it has limited application domains constrained to printed text. This is partly due to high time complexity of Arabic OCR algorithms. Accelerating these algorithms allows for applications in real-time assistive technologies, office automation, and portable devices. Modern programmable hardware devices like FPGAs have numerous logic resources that allow parallel implementations of many algorithms. In this paper, we investigate implementing the feature extraction and classification stages of handwritten Arabic words on FPGAs. We study the performance and cost of four commonly-used feature extraction techniques and neural network classifiers on images from the IFN/ENIT database of handwritten Arabic words. The most efficient feature extraction technique and the best neural network found are implemented. Multiple FPGA implementations with varying cost and performance are evaluated. An implementation that only consumes about one quarter of the FPGA resources is 20 times faster than the software implementation and is less accurate by only 2.8 %.

Learning-based word spotting system for Arabic handwritten documents

The retrieval of information from scanned handwritten documents is becoming vital with the rapid increase of digitized documents, and word spotting systems have been developed to search for words within documents. These systems can be either template matching algorithms or learning based. This paper presents a coherent learning based Arabic handwritten word spotting system which can adapt to the nature of Arabic handwriting, which can have no clear boundaries between words. Consequently, the system recognizes Pieces of Arabic Words (PAWs), then re-constructs and spots words using language models. The proposed system produced promising result for Arabic handwritten word spotting when tested on the CENPARMI Arabic documents database.

A Multi-Stream Hmm Approach to Offline Handwritten Arabic Word Recognition

In This paper we presented new approach for cursive Arabic text recogniti on system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation. The first part in the writing recognition system is the preprocessing phaseis the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by twomethods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diag onals 45° and 135°) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi -stream approach.

Effective technique for the recognition of offline Arabic handwritten words using hidden Markov models

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 new technique for dividing the image into nonuniform horizontal segments to extract the features and a new technique for solving the problems of the skewing of characters by fusing multiple HMMs. Moreover, two enhancements 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 nonuniform horizontal segments depending 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 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. Besides the vertical sliding window, two slanted sliding windows are used to extract the features. Three different 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.

Tuning Recurrent Neural Networks for Recognizing Handwritten Arabic Words

Artificial neural networks have the abilities to learn by example and are capable of solving problems that are hard to solve using ordinary rule-based programming. They have many design parameters that affect their performance such as the number and sizes of the hidden layers. Large sizes are slow and small sizes are generally not accurate. Tuning the neural network size is a hard task because the design space is often large and training is often a long process. We use design of experiments techniques to tune the recurrent neural network used in an Arabic handwriting recognition system. We show that best results are achieved with three hidden layers and two subsampling layers. To tune the sizes of these five layers, we use fractional factorial experiment design to limit the number of experiments to a feasible number. Moreover, we replicate the experiment configuration multiple times to overcome the randomness in the training process. The accuracy and time measurements are analyzed and modeled. The two models are then used to locate network sizes that are on the Pareto optimal frontier. The approach described in this paper reduces the label error from 26.2% to 19.8%.

A Model-Based Sequence Similarity with Application to Handwritten Word Spotting

This paper proposes a novel similarity measure between vector sequences. We work in the framework of model-based approaches, where each sequence is first mapped to a Hidden Markov Model (HMM) and then a measure of similarity is computed between the HMMs. We propose to model sequences with semicontinuous HMMs (SC-HMMs). This is a particular type of HMM whose emission probabilities in each state are mixtures of shared Gaussians. This crucial constraint provides two major benefits. First, the a priori information contained in the common set of Gaussians leads to a more accurate estimate of the HMM parameters. Second, the computation of a similarity between two SC-HMMs can be simplified to a Dynamic Time Warping (DTW) between their mixture weight vectors, which significantly reduces the computational cost. Experiments are carried out on a handwritten word retrieval task in three different datasets-an in-house dataset of real handwritten letters, the George Washington dataset, and the IFN/ENIT dataset of Arabic handwritten words. These experiments show that the proposed similarity outperforms the traditional DTW between the original sequences, and the model-based approach which uses ordinary continuous HMMs. We also show that this increase in accuracy can be traded against a significant reduction of the computational cost.

Handwriting word recognition using windowed Bernoulli HMMs

Hidden Markov Models (HMMs) are now widely used for off-line handwriting recognition in many languages. As in speech recognition, they are usually built from shared, embedded HMMs at symbol level, where state-conditional probability density functions in each HMM are modeled with Gaussian mixtures. In contrast to speech recognition, however, it is unclear which kind of features should be used and, indeed, very different features sets are in use today. Among them, we have recently proposed to directly use columns of raw, binary image pixels, which are directly fed into embedded Bernoulli (mixture) HMMs, that is, embedded HMMs in which the emission probabilities are modeled with Bernoulli mixtures. The idea is to by-pass feature extraction and to ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. In this work, column bit vectors are extended by means of a sliding window of adequate width to better capture image context at each horizontal position of the word image. Using these windowed Bernoulli mixture HMMs, good results are reported on the well-known IAM and RIMES databases of Latin script, and in particular, state-of-the-art results are provided on the IfN/ENIT database of Arabic handwritten words.

Text‐Dependent Writer Identification for Arabic Handwriting

This paper proposes a system for text‐dependent writer identification based on Arabic handwriting. First, a database of words was assembled and used as a test base. Next, features vectors were extracted from writers′ word images. Prior to the feature extraction process, normalization operations were applied to the word or text line under analysis. In this work, we studied the feature extraction and recognition operations of Arabic text on the identification rate of writers. Because there is no well‐known database containing Arabic handwritten words for researchers to test, we have built a new database of offline Arabic handwriting text to be used by the writer identification research community. The database of Arabic handwritten words collected from 100 writers is intended to provide training and testing sets for Arabic writer identification research. We evaluated the performance of edge‐based directional probability distributions as features, among other characteristics, in Arabic writer identification. Results suggest that longer Arabic words and phrases have higher impact on writer identification.

Freeman Chain Code Contour Processing for Handwritten Isolated Arabic Characters Recognition

The Arabic characters are being used in various languages. Recognition of handwritten characters of Arabic alphabet set is an important area of research. The work on recognition of Handwritten Isolated Arabic Characters (HIAC) is still an open research problem and it has numerous applications. Machine reading of optically scanned text is usually called optical Character recognition (OCR). In this paper we present an OCR for Handwritten Isolated Arabic Characters. Basic Characters are recognized by Freeman chain code. The proposed method obtains the contour of the character and then generates a Freeman Chain Code for contoured character. The obtained Freeman chain code is unique for handwritten isolated characters. The present method is based on template matching to recognize handwritten printed isolated Arabic characters. It was trained and validated on 200 images (consisting of 7400 Arabic characters written by 18 writers). Our experimentation observed the overall recognition rate is 95%. It is carried out in order to improve the rate and the performance of an Arabic handwritten word recognition system. The proposed system has been implemented and tested on Matlab R2008b environment.