Structural Features Extraction for Handwritten Arabic Personal Names Recognition

This paper presents an overview of feature extraction techniques for unconstrained Arabic handwritten word recognition. Choosing a technique for extraction the features considers the most important factor in achieving high recognition rates in word or character recognition. Different techniques were designed to extract the features from the Arabic words. These techniques are presented and discussed in terms of invariant invariance properties.

In this work, we investigate the combination of PGM (Propabilistic Graphical Models) classifiers, either independent or coupled, for the recognition of Arabic handwritten words. The independent classifiers are vertical and horizontal HMMs (Hidden Markov Models) whose observable outputs are features extracted from the image columns and the image rows respectively. The coupled classifiers associate the vertical and horizontal observation streams into a single DBN (Dynamic Bayesian Network). A novel method to extract word baseline and a simple and easily extractable features to construct feature vectors for words in the vocabulary are proposed. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders, loops and diacritic points. Experiments on handwritten Arabic words from IFN/ENIT strongly support the feasibility of the proposed approach. The recognition rates achieve 90.42% with vertical and horizontal HMM, 85.03% and 85.21% with respectively a first and a second DBN which outperform results of some works based on PGMs.

<span lang="EN-US">Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the </span><em><span lang="EN-US">Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis</span></em><span lang="EN-US"> (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words.</span>

Hidden Markov Models (HMMs) are now widely used in off-line handwriting recognition and, in particular, in Arabic handwritten word recognition. In contrast to the conventional approach, based on Gaussian mixture HMMs, we have recently proposed to directly fed columns of raw, binary pixels into Bernoulli mixture HMMs. 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, very good results are reported on the well-known IfN/ENIT database of Arabic handwritten Tunisian town names.

Offline Arabic handwritten word recognition: A transfer learning approach

In this paper, an Arabic handwritten word recognition system for wide vocabulary using the analytical approach is presented. After the segmentation phase, the characters are recognized using assumption generation of letters. Thus, the proposed system generates assumptions of words by concatenation of letters assumption. Thereafter, a filtering of these words assumption is achieved thanks to a language dictionary. The system showed good performances when applied on a 500 word images dataset.

Hidden Markov models (HMMs) are now widely used for off-line handwriting recognition in many languages and, in particular, in Arabic. As in speech recognition, they are usually built from shared, embedded HMMs at the symbol level, in which state-conditional probability density functions are modeled with Gaussian mixtures. In contrast to speech recognition, however, it is unclear which kinds of features should be used and, indeed, very different feature sets are in use today. Among them, we have recently proposed to simply 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 bypass feature extraction and ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. In this chapter, we review this idea along with some extensions that are currently providing state-of-the-art results on Arabic handwritten word recognition.

<span>Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.</span>

In the paper a new method of handwritten word recognition is described. In this method different probabilistic character language models (PCLMs) are used in order to improve the word recognition accuracy. These models consist of conditional probability distributions of characters given that preceding, succeeding or surrounding characters are known. The application of character succession and precedence in word recognition was explored in many earlier works. The novelty of the method proposed here consists in utilizing also the conditional probabilities based on character appearing of both sides of the letter being recognized and in combining such classifier with other ones based on character precedence and succession. This new classifier that uses two sided character neighborhood is realized as an iterative procedure which starts with character support factors evaluated independently for each character in the word by a soft character classifier. Then in each step of this iterative procedure the support factors are calculated as Bayes-like formula using needed probabilities. Next, the support factors obtained at the character level are used to calculate the support factors for complete words. Finally, the soft word recognition results obtained for classifiers based on three different PCLMs are combined. Experiments described in the paper show the superiority of the combined method over all its simpler components.

A system for writer identification based on Arabic handwritten words was built. First a database of words was gathered and used as a test base. Then, features vectors were extracted from writers' word images. Prior to feature extraction, normalization operations were applied to a word or text line. In this research, we studied the feature extraction and recognition operations on Arabic text, on the identification rate of writers. Since there is no well known database containing Arabic handwritten words for researchers to test, we built a new database of off-line Arabic handwriting text to be used for writer identification research. The proposed database is meant to provide training and testing sets for Arabic writer identification research. Arabic handwritten words were collected from 100 writers. We evaluated the performance of edge-based directional probability distributions as features and other features in Arabic writer identification.

We propose a fast stroke-based method for off-line handwritten word recognition, particularly adapted for use with semantic educational video indexing (blackboard and hand drawn slides taken from low quality videos). We first extract a skeleton from each handwritten word image, then break the skeleton into a sequence of strokes. For each candidate vocabulary word, we use a dynamic programming algorithm on the stroke sequence to incorporate character segmentation and recognition into one procedure, where we find the optimal segmentation and similarity score between word and image simultaneously. Although our method is independent of the implementation of the handwritten character recognition(HCR) module itself, we also propose a stroke-based HCR approach. Stroke matching is based on a set of semantic rules, which are fast and robust to writing styles. We evaluate the overall approach on three different training sets, and the results in this difficult domain are encouraging.

In this paper, we propose an orientation free method for unconstrained cursive handwritten chinese word recognition. By a novel gravity center balancing method, the orientation of a handwritten word can be detected. Through the process of stroke extraction, stroke breaking, heuristic over-segmentation and path searching by recognition and lexicon information, the handwritten word with characters even connected or partially overlapped can be recognized. Experiments were performed on 173,660 unconstrained handwritten Chinese word samples collected by Pocket PC. The promising experimental results demonstrated our method is an orientation free and stroke-order free method for unconstrained cursive handwritten Chinese word recognition.

Speaker independent recognition of low-resourced multilingual Arabic spoken words through hybrid fusion

Arabic handwritten text recognition is a complex and challenging research domain. This study proposes an offline Arabic handwritten word recognition system based on transfer learning. The system exploits four pre-trained convolutional neural network (CNN) architectures, namely VGG16, ResNet50, AlexNet, and InceptionV3. In addition, a specialized image recognition model derived from the ImageNet dataset is incorporated. A combination strategy is designed to combine transfer learning with specific fine-tuning techniques, aiming to improve recognition accuracy. The study is conducted on the IFN/ENIT dataset, which includes images of Tunisian City and village names. The results show that the proposed system achieves a recognition accuracy of 94.73%, which is significantly higher than the accuracy rates achieved by previous approaches. These results suggest that the proposed system is a promising approach for Arabic handwritten text recognition.

Freeman Chain Code Contour Processing for Handwritten Isolated Arabic Characters Recognition