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In a computer vision system, handwritten digits recognition is a complex task that is central to a variety of emerging applications. It has been widely used by machine learning and computer vision researchers for implementing practical applications like computerized bank check numbers reading. In this study, we implemented a multi-layer fully connected neural network with one hidden layer for handwritten...
Arabic character recognition is one of the most challenging tasks and exciting areas of research. In this one, we will present a mobile application for handwriting recognition. Our Mobile application proposes an approach for Arabic handwriting recognition based on the fact that handwriting is defined as a sequence of elementary and perceptual codes. After applying PerTOHS theory which is a perceptual...
Automatic character recognition of handwritten numerals and characters has been an active subject of research due to its importance on industrial as well as educational platform. The off-line handwritten character recognition is an active area for research towards the new techniques that would help to improve recognition accuracy. Now a day's looking forward for rapidly growing technologies, with...
In this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution...
This paper describes an online handwritten cursive word recognition approach by combining segmentation-free and segmentation-based methods. To search the optimal segmentation and recognition path as the recognition result, we can attempt two methods: segmentation-free and segmentation-based, where we expand the search space using a character-synchronous beam search strategy. The probable search paths...
In this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution...
Devanagari script is being used in various languages, in South Asian subcontinent, such as Sanskrit, Rajasthan, Marathi and Nepali and it is also the script of Hindi, the mother tongue of majority of Indians. Recognition of handwritten words of Devanagari script is an important area of research. A person who knows the script of a language can easily read the hand-printed words pertaining to that script...
In this paper we are going to apply four descriptors (GIST, PHOG, SURF and Centrist) and two classifiers (Artificial Neural Network (ANN) and Support Vector Machines (SVM)) for handwritten mathematical symbols recognition to achieve a comparative study based on the recognition rate.
In this paper we proposed SVM algorithm for MNIST dataset with fringe and its complementary version, inverse fringe as feature for SVM. MNIST data-set is consists of 60000 examples of training set and 10000 examples of test set. In our experiments we started with using fringe distance map as feature and found that the accuracy of system on trained data is 99.99% and on test data it is 97.14%, using...
Human characteristics forms a base for various biometric traits. Authenticating a human by making the use of his biometric trait for various identification and access control processes provides increased security. Individuals in a group are identified by using biometrics. Biometric identifiers are used to measure and identify human traits. Identifiers can be classified in two types physiological versus...
This article presents the development of an Arabic online handwriting recognition system based on neural networks approach. It offers solutions for most of the difficulties linked to Arabic script recognition. Secondly, our proposed system will be integrated in Arabic language learning tool to create educational activities and to generate adequate feedback.
Optical Character Recognition alludes to the methodology of taking images or photos of letters or typewritten content and changing over them into information that a machine can easily interpret, e.g. organizations and libraries taking physical duplicates of books, magazines, or other old printed material and utilizing OCR to put them into computers. Segmentation is the indispensable and most difficult...
This paper proposes an Intelligent Handwriting Thai Signature Recognition System base on Multilayer Perceptron and Radial Basis Network. The proposed system compose of three main processes, i.e. image pre-processing, feature extraction and Thai signature recognition. In the recognition processes the neural network is used into two stage. First, Multilayer Perceptron (MLP) and Radial Basis Function...
This paper presents the results of the HDSRC 2014 competition on handwritten digit string recognition in challenging datasets organized in conjunction with ICFHR 2014. The general objective of this competition is to identify, evaluate and compare recent developments in Western Arabic digit string recognition with varying length. In addition, this competition introduces two new challenging datasets...
This paper introduces new handwritten databases of selected words in the five Middle-Eastern languages of Arabic, Dari, Farsi, Pashto and Urdu. The databases share a common lexicon of forty words that are related to finance and are used in daily life. The five databases have been collected from over 1600 native writers located in four countries. Recognition results for each of the databases are also...
Chinese Calligraphy draws a lot of attention for its beauty and elegance. Until now, lot of people don't know the semantic meaning of many calligraphic characters because they were written in ancient times. Technologies which can help users to recognize the unknown calligraphic characters are urgently required. However recognizing calligraphic characters is a challenging work due to the reasons: (1)complication...
Bangla handwritten character recognition is one of the complex works because of the wide variation of the Bangla character. In this paper we proposed a new approach for extracting the features of Bangla handwritten characters and then recognition of those characters using artificial neural network has done. For the feature extraction process we have used a row and column basis segmentation process...
We propose a segmentation based online word recognition approach which uses a Conditional Random Field (CRF) driven beam search strategy. An efficient trie-lexicon directed, breadth-first beam search algorithm is employed in a combined segmentation-and-recognition framework to accomplish real-time recognition of online handwritten cursive English words. This framework is developed by building a candidate...
Automatic off-line Arabic handwriting recognition based on segmentation still faces big challenges. A database, covering all shapes of handwritten Arabic characters, is required to facilitate the recognition process. This paper introduces a new database for handwritten Arabic characters (HACDB), designed to cover all shapes of Arabic characters including overlapping ones. It contains 6,600 shapes...
This paper aims that analysing neural network method in pattern recognition. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. The proposed solutions focus on applying Bidirectional Associative Memory method model for pattern recognition. The primary function of which is to retrieve in a pattern stored in memory, when...
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