Freeman Chain Code Contour Processing for Handwritten Isolated Arabic Characters Recognition

Due to the several forms of different Arabic font types, Arabic character recognition is still a challenge. Most literature works consider only one font per text what results in low recognition accuracy. This paper tends to enhance the accuracy of AOCR (Arabic Optical Character Recognition) by considering an automatic Optical Font Recognition (OFR) stage before going ahead with the traditional OCR stages. This has been achieved using SIFT (Scale Invariant Feature Transform) descriptors. First, a comparative study of four most recent algorithms of primitive OCR has been performed to evaluate the different features and classifiers utilized in their systems. Accordingly, a combining of statistical features have been proposed as well as selecting Random Forest Tree classifier for classification stage. The combination of the features are used to train the classifiers. As a result, each recognized text font is directed to a specific classifier tree. The proposed system was tested on a generated Primitive Arabic Characters Noise Free dataset (PAC-NF) containing 30000 samples. Experimental results achieved a promising character recognition accuracy of 99.8–100%.

Car plate recognition (CPR) system is an important application of image detection and recognition used to overcome the challenges of monitoring modern day traffic. The Freeman chain codes (FCC) is applied in this research to study on the accuracy and efficiency of this technique as an alternative recognition approach to recognize characters on car plates because of its ability to recognize characters and digits successfully. Therefore, this paper is mainly focused on conducting an experiment using FCC to test on its accuracy and efficiency in characters recognition with the support of characters’ features because FCC alone does not provide an accurate and efficient recognition result. The result shows that the combination of FCC with characters’ features (FCCwF) yields 95% recognition accuracy. As a conclusion, FCC can be an accurate and efficient technique only if the image quality is high and without any noise which might disturbing the recognition process or if it is combined with another technique.KeywordsImage processingrecognitionchain codessegmentationfeatures

This manuscript considers a new architecture to handwritten characters recognition based on simulation of the behavior of one type of artificial neural network, called the Error Back Propagation Artificial Neural Network (EBPANN). We present an overview of our neural network to be optimized and tested on 12 offline isolated Arabic handwritten characters (???????????????? ?,?,???) because the similarity of some Arabic characters and the location of the points in the character. Accuracy of 93.61% is achieved using EBPANN which is the highest accuracy achieved during Offline Handwritten Arabic Character Recognition. It is noted that the EBPANN in general generates an optimized comparison between the input samples and database samples which improves the final recognition rate. Experimental results show that the EBPANN is convergent and more accurate in solutions that minimize the error recognition rate.

Optical Character Recognition (OCR) deals with automated recognition of characters that are in the format of digital image. OCR refers to the process by which scanned images are electronically processed and converted to an editable document. Handwritten and printed texts are the primary research areas of an OCR. Many OCR systems are commercially available for English and Arabic characters but there is still no recognition system available which yields higher recognition rate even though the scanned images are of high quality. The general framework of a Tamil OCR in the literature involves: preprocessing, line segmentation, word segmentation, character segmentation, feature extraction and recognition of characters. OCR for printed Tamil documents poses challenge owing to: one line may have different font styles, presence of pictures, multi columns, touching of adjacent characters, presence of broken characters, low print quality and complex layout. Furthermore, when comparing 26 alphabets in English, Tamil language has 247 alphabets which makes the recognition more difficult. There are few OCRs for Tamil language that are freely available with a moderate recognition rate as the performance comparisons of such OCRs are not available on a benchmark dataset. In this paper we compare OCRs for printed Tamil texts on four different types of documents: books, magazines, newspapers and pamphlets. Furthermore we propose a post-processing error correction technique to the tested OCRs which reduces the overall mean error rate by nearly 10% on those four categories.

Optical Characters Recognition (OCR) is one of the active subjects of research since the early days of computer science. Even if Arabic characters are used by more than a half a billion people; Arabic characters recognition has not received enough interests by the researchers. Little research progress has been achieved comparing to what has been done with Latin and Chinese. The cursive nature of the Arabic characters makes it more difficult to achieve a high accuracy in character recognition since even printed Arabic characters are in cursive form. This paper presents the main challenges (difficulties) researchers are facing and up to dated solutions (the common methods) are used for Arabic text recognition.

 Abstract—Automatic License Plate (LP) recognition uses optical character recognition to read LPs on vehicles, such system is essential for traffic control, theft control, electronic toll collection, parking lots access and/or billing. The proposed Arabic LP recognition system is applied to Saudi Arabian LPs which have two different formats. Therefore, this system recognizes both Arabic and Indian numerals as well as limited Arabic and Latin alphabets. The system goes through many preprocessing steps in order to produce segmented characters of the LPs images. The feature extraction step uses the count of black pixels from the horizontal projection profiles in addition to the black pixel distributions in divided zones of the character image. The recognition is performed separately based on different regions; therefore, applying the recognition process to specific regions of the LPs; speeds up the processing due to the limited number of comparisons. The recognition step uses both a distance classifier and Neural Network (NN) classifier to discriminate between the different characters. This proposed system provides more importance to the preprocessing stage whose success guarantees the successful performance of the whole system. Also, the Arabic characters are verified against the corresponding Latin characters and similarly the Arabic numerals to their corresponding Indian numerals. The system is tested on 462 correctly localized LPs providing a 98.63% character recognition rate and a total of 94.9% accepted LPs.

This paper presents an optical Bangla character recognition (OCR) system using Freeman Chain Code and feed forward back propagation neural network which provides much higher performance than some other traditional approaches. After scanned the printed Bangla document, preprocessing and segmentation has done. In the feature extraction stage Freeman Chain Code is used. Finally, feed forward back propagation neural network is used in classification of Bangla character. Performances results are presented in the literature.

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.

On-line Chinese character recognition using ART-based stroke classification

This paper proposes and contributes towards designing a complete system for off-line Arabic character recognition. The proposed system is specifically meant for Arabic handwriting recognition, but it equally works for the typed character recognition. It has various phases including preprocessing and segmentation. It also includes thinning phase and finds vertical and horizontal projection profiles. The recognition phase is managed by genetic algorithm. The genetic algorithm stands on feature extraction algorithm that defines six features for each segment. The algorithm, for Arabic handwriting recognition, obtained 90.46 recognition rate. The proposed system has been compared with other systems in the literature. It has achieved the second best recognition rate.

This paper proposes and contributes towards designing a complete system for off-line Arabic character recognition. The proposed system is specifically meant for Arabic handwriting recognition, but it equally works for the typed character recognition. It has various phases including preprocessing and segmentation. It also includes thinning phase and finds vertical and horizontal projection profiles. The recognition phase is managed by genetic algorithm. The genetic algorithm stands on feature extraction algorithm that defines six features for each segment. The algorithm, for Arabic handwriting recognition, obtained 90.46 recognition rate. The proposed system has been compared with other systems in the literature. It has achieved the second best recognition rate.

<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>

Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) are technologies that enable text recognition. The difference between OCR and HTR is one designed specifically for digital text and one designed for handwritten text. There are already various implementations of OCR and HTR online. However, such systems do not guarantee the systems are in premises. To solve this problem, the OCR and HTR system must be built from the scratch. The purpose of this research is to improve the recognition by separating the text whether it is a handwritten or a printed text, which will later be forwarded into the appropriate recognition system. An application program interface (API) was also created in order to finalize the classification system into real world usage. In this research, the classification system being developed using convolutional neural network (CNN) method. To be able to reach the highest accuracy of the classification system, the experimentation and improvement about hyperparameters, dataset format, data augmentation and analysis on 3 CNN architectures were conducted. In the end of this research, there are 2 architectures in a tight competition, one is VGG-16 with 90.63% accuracy and one is AlexNet with 90.17% accuracy on ideal data testing. However, AlexNet is chosen as the winner after the testing with real data.

We present an original method of identity signatures extraction of online handwritten Arabic and Latin characters based on the frequent pattern mining, through the “Apriori” algorithm. This work is based on models that we have generated, to describe the writer behavior that illustrates a trajectory, from local and global features which include temporal information. We also manipulated the minimum threshold value, which is an essential parameter in frequent patterns extraction algorithms, to obtain the dominant spatiotemporal features of the character identity. In addition, we have incorporated the patterns frequency called “support” as a new statistical feature relevant in the signature. These signatures can also provide portability and add a great flexibility to systems that adopt this type of modeling. The obtained results are very promising as we have achieved very high rates of correct recognition and the models which generated these results are considered as relevant and reliable models.

This paper concerns with the online recognition of isolated hand writing Arabic characters, through, the interpretation of a script presented by a pen trajectory. This technique was generally used in the electronic organizers of Personal Digital Assistant type. First of all, we have built a data base with several scripters using a graphic tablet which will be used in our application. In order to have a precise recognition of the isolated characters, it is important to model their structure the most correctly possible. In this work we present the study, the implementation and the result of the test of a particular neural network which is the Time Delay Neural Networks. We have followed a two steps approach, in the first one, the character characteristics are extracted, and in the second one, a temporal multi-layered perception is developed for a future classification. Our temporal approach with the adaptive topology, responds to the nature of the Arabic script during the acquisition phase while the use of different learning algorithms can minimize the cost function and improve recognition rates. The parameterization of these two parts will allow us to analyze the impact of the neural network topology on the results of character recognition rates.