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AltecOnDB: A Large-Vocabulary Arabic Online Handwriting Recognition Database  [PDF]
Ibrahim Abdelaziz,Sherif Abdou
Computer Science , 2014,
Abstract: Arabic is a semitic language characterized by a complex and rich morphology. The exceptional degree of ambiguity in the writing system, the rich morphology, and the highly complex word formation process of roots and patterns all contribute to making computational approaches to Arabic very challenging. As a result, a practical handwriting recognition system should support large vocabulary to provide a high coverage and use the context information for disambiguation. Several research efforts have been devoted for building online Arabic handwriting recognition systems. Most of these methods are either using their small private test data sets or a standard database with limited lexicon and coverage. A large scale handwriting database is an essential resource that can advance the research of online handwriting recognition. Currently, there is no online Arabic handwriting database with large lexicon, high coverage, large number of writers and training/testing data. In this paper, we introduce AltecOnDB, a large scale online Arabic handwriting database. AltecOnDB has 98% coverage of all the possible PAWS of the Arabic language. The collected samples are complete sentences that include digits and punctuation marks. The collected data is available on sentence, word and character levels, hence, high-level linguistic models can be used for performance improvements. Data is collected from more than 1000 writers with different backgrounds, genders and ages. Annotation and verification tools are developed to facilitate the annotation and verification phases. We built an elementary recognition system to test our database and show the existing difficulties when handling a large vocabulary and dealing with large amounts of styles variations in the collected data.
A Hybrid NN/HMM Modeling Technique for Online Arabic Handwriting Recognition  [PDF]
Najiba Tagougui,Houcine Boubaker,Monji Kherallah,Adel M. ALIMI
Computer Science , 2014,
Abstract: In this work we propose a hybrid NN/HMM model for online Arabic handwriting recognition. The proposed system is based on Hidden Markov Models (HMMs) and Multi Layer Perceptron Neural Networks (MLPNNs). The input signal is segmented to continuous strokes called segments based on the Beta-Elliptical strategy by inspecting the extremum points of the curvilinear velocity profile. A neural network trained with segment level contextual information is used to extract class character probabilities. The output of this network is decoded by HMMs to provide character level recognition. In evaluations on the ADAB database, we achieved 96.4% character recognition accuracy that is statistically significantly important in comparison with character recognition accuracies obtained from state-of-the-art online Arabic systems.8
AlexU-Word: A New Dataset for Isolated-Word Closed-Vocabulary Offline Arabic Handwriting Recognition  [PDF]
Mohamed E. Hussein,Marwan Torki,Ahmed Elsallamy,Mahmoud Fayyaz
Computer Science , 2014,
Abstract: In this paper, we introduce the first phase of a new dataset for offline Arabic handwriting recognition. The aim is to collect a very large dataset of isolated Arabic words that covers all letters of the alphabet in all possible shapes using a small number of simple words. The end goal is to collect a very large dataset of segmented letter images, which can be used to build and evaluate Arabic handwriting recognition systems that are based on segmented letter recognition. The current version of the dataset contains $25114$ samples of $109$ unique Arabic words that cover all possible shapes of all alphabet letters. The samples were collected from $907$ writers. In its current form, the dataset can be used for the problem of closed-vocabulary word recognition. We evaluated a number of window-based descriptors and classifiers on this task and obtained an accuracy of $92.16\%$ using a SIFT-based descriptor and ANN.
Offline Arabic Handwriting Recognition Using Artificial Neural Network  [PDF]
A. A Zaidan,B. B Zaidan,Hamid. A. Jalab,Hamdan. O. Alanazi,Rami Alnaqeib
Computer Science , 2010,
Abstract: The ambition of a character recognition system is to transform a text document typed on paper into a digital format that can be manipulated by word processor software Unlike other languages, Arabic has unique features, while other language doesn't have, from this language these are seven or eight language such as ordo, jewie and Persian writing, Arabic has twenty eight letters, each of which can be linked in three different ways or separated depending on the case. The difficulty of the Arabic handwriting recognition is that, the accuracy of the character recognition which affects on the accuracy of the word recognition, in additional there is also two or three from for each character, the suggested solution by using artificial neural network can solve the problem and overcome the difficulty of Arabic handwriting recognition.
Off-Line Arabic Handwriting Character Recognition Using Word Segmentation  [PDF]
Manal A. Abdullah,Lulwah M. Al-Harigy,Hanadi H. Al-Fraidi
Computer Science , 2012,
Abstract: The ultimate aim of handwriting recognition is to make computers able to read and/or authenticate human written texts, with a performance comparable to or even better than that of humans. Reading means that the computer is given a piece of handwriting and it provides the electronic transcription of that (e.g. in ASCII format). Two types of handwriting: on-line and offline. The most important purpose of off-line handwriting recognition is in protection systems and authentication. Arabic Handwriting scripts are much more complicated in comparison to Latin scripts. This paper introduces a simple and novel methodology to authenticate Arabic handwriting characters. Reaching our aim, we built our own character database. The research methodology depends on two stages: The first is character extraction where preprocessing the word and then apply segmentation process to obtain the character. The second is the character recognition by matching the characters comprising the word with the letters in the database. Our results ensure character recognition with 81%. We eliminate FAR by using similarity percent between 45-55%. Our research is coded using MATLAB.
An Empirical Evaluation of Off-line Arabic Handwriting And Printed Characters Recognition System  [PDF]
Firoj Parwej
International Journal of Computer Science Issues , 2012,
Abstract: Handwriting recognition is a challenging task for many real-world applications such as document authentication, form processing, historical documents. This paper focuses on the comparative study on off-line handwriting recognition system and Printed Characters by taking Arabic handwriting. The off-line Handwriting Recognition methods for Arabic words which being often used among then across the Middle East and North Africa people. In this paper we are proposing off-line Arabic handwriting and printed characters and the language used by the majority of the Middle East. We are using discrete Hidden Markov Models (HMM) for Arabic handwriting and printed characters for the final recognition. In this paper after preprocessing step the characters are auto-segmented using a recursive algorithm as sequences of connected neighbors along lines and curves and Arabic words are first pre-classified into one of known character groups, based on the structural properties of the text line. The proposed system was trained and tested Arabic character images. The Arabic characters were written by the different people on a preformatted paper and the method recognizes the Arabic handwriting in print style format. A comparative Experimental result has shown 93.40% recognition rate for the Arabic handwriting and 97.30% recognition rate for the Arabic printed characters.
The State of the Art Recognize in Arabic Script through Combination of Online and Offline  [PDF]
Dr. Firoj Parwej
Computer Science , 2013,
Abstract: Handwriting recognition refers to the identification of written characters. Handwriting recognition has become an acute research area in recent years for the ease of access of computer science. In this paper primarily discussed On-line and Off-line handwriting recognition methods for Arabic words which are often used among then across the Middle East and North Africa People. Arabic word online handwriting recognition is a very challenging task due to its cursive nature. Because of the characteristic of the whole body of the Arabic script, namely connectivity between the characters, thereby the segmentation of An Arabic script is very difficult. In this paper we introduced an Arabic script multiple classifier system for recognizing notes written on a Starboard. This Arabic script multiple classifier system combines one off-line and on-line handwriting recognition systems. The Arabic script recognizers are all based on Hidden Markov Models but vary in the way of preprocessing and normalization. To combine the Arabic script output sequences of the recognizers, we incrementally align the word sequences using a norm string matching algorithm. The Arabic script combination we could increase the system performance over the excellent character recognizer by about 3%. The proposed technique is also the necessary step towards character recognition, person identification, personality determination where input data is processed from all perspectives.
Recurrent Neural Network Method in Arabic Words Recognition System  [PDF]
Yusuf Perwej
Computer Science , 2013,
Abstract: The recognition of unconstrained handwriting continues to be a difficult task for computers despite active research for several decades. This is because handwritten text offers great challenges such as character and word segmentation, character recognition, variation between handwriting styles, different character size and no font constraints as well as the background clarity. In this paper primarily discussed Online Handwriting Recognition methods for Arabic words which being often used among then across the Middle East and North Africa people. Because of the characteristic of the whole body of the Arabic words, namely connectivity between the characters, thereby the segmentation of An Arabic word is very difficult. We introduced a recurrent neural network to online handwriting Arabic word recognition. The key innovation is a recently produce recurrent neural networks objective function known as connectionist temporal classification. The system consists of an advanced recurrent neural network with an output layer designed for sequence labeling, partially combined with a probabilistic language model. Experimental results show that unconstrained Arabic words achieve recognition rates about 79%, which is significantly higher than the about 70% using a previously developed hidden markov model based recognition system.
A Three Stages Segmentation Model for a Higher Accurate off-line Arabic Handwriting Recognition
Said Elaiwat,Marwan AL-abed Abu-zanona,Farah Hanna AL-Zawaideh
World of Computer Science and Information Technology Journal , 2012,
Abstract: Arabic handwriting recognition considers a one of the hardest applications of OCR system. The reason of that relates to characteristics of Arabic characters and the way of writing cursively. Furthermore, no rules can control on handwriting way, different styles, sizes and curves make the process of recognition is very complex. On other side, the key for reaching to good recognition is by getting a correct segmentation. Actually, the way of segmentation is important, because if there is a small part is not clear in character that will reflect on recognition process. In this paper we aim to enhance the accuracy of off-line Arabic Hand Written text segmentation. Three stages are proposed to reach to highest ratio of segmentation. Line segmentation is the first stage, where it is proposed to separate each line. We depend on row density to predict spaces among lines. Second stage is Object segmentation and it is proposed to segment each word or sub word. Eight neighbors connectivity are used to detect connected pixels. Final stage is shape segmentation which is proposed to segment sub word to characters. The idea in this stage is finding segmentation points among branch points in the baseline. To apply that we propose four threshold values to investigate on each branch point. The result was satisfactory and the model proved a good ability to tackle different types of texts with bad samples.
Distributing Arabic Handwriting Recognition System Based on the Combination of Grid Meta-Scheduling and P2P Technologies (Omnivore)  [PDF]
Hassen Hamdi,Maher Khemakhem
Universal Journal of Computer Science and Engineering Technology , 2010,
Abstract: Character recognition is one of the oldest fields of research. It is the art of automating both the process of reading and keyboard input of text in documents. A major part of information in documents is in the form of alphanumeric text. Significant movement has been made in handwriting recognition technology over the last few years. Up until now, Arabic handwriting recognition systems have been limited to small and medium size of documents to recognize. The facility of dealing with large database (large scale), however, opens up many more applications. Our idea consists to use a strong and complimentary approach which needs enough computing power. We have used a distributed Arabic handwriting system based on the combination of Grid meta-scheduling and Peer–to-Peer (P2P) technologies such as Omnivore. Obtained results confirm that our approach present a very interesting framework to speed up the Arabic optical character recognition process and to integrate (combine) strong complementary approaches which can lead to the implementation of powerful handwriting OCR systems .
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