Optical character recognition Shrestha, Pramoj The concept behind OCR is to acquire a document in image or pdf formats and extract the characters from that image and present it to the user in an editable format.
The rediscovery of the backpropagation algorithm was probably the main reason behind the repopularisation of neural networks after the publication of "Learning Internal Representations by Error Propagation" in Though backpropagation itself dates from As a result, there was more funding and thus more research in the field. An efficient preprocessing stage has been proposed for the Arabic handwritten text and successfully applied to the proposed database. The input to the system is an Arabic text handwritten text image which passes into the both modules, where the output of the first module is editable Arabic text and the second one is the writer of the input Arabic handwritten text document. In handwritten, the spacing between the different semi-words of the same sub-word is smaller than the spacing between two different sub-words. To operate correctly, the network should respond with a 1 in the position of the letter being presented to the network. Everything from handwriting and speech recognition to stock market prediction will become more sophisticated as researchers develop better training methods and network architectures.
The author of this thesis tested an Artificial Neural Network ANN , which is a mathematical representation of the functionality of the human brain, using Back-propagation Algorithm with test case files of English alphabets. On the other hand, alignment techniques in the literatures are based on approximation while the voting process is not context-aware. These drawbacks lead to a high error rate in OCR. This research proposed three improved techniques of differentiation, alignment, and voting to overcome the identified drawbacks.
These techniques were later combined into a hybrid model that can recognize the optical characters in the Arabic language. Each of the proposed technique was separately evaluated against three other relevant existing techniques. Experimental results showed a relative decrease in error rate on all measurements for the evaluated techniques.
Output layer: The neurons in this layer receive signals from neurons either in the input layer or in the hidden layer.
Hidden layer: The layer of neurons that are connected in between the input layer and the output layer is known as hidden layer. Neural nets are often classified as single layer networks or multilayer networks. The number of layers in a net can be classified as the number of layers of weighted interconnection links between various layers. This is the most general neural net architecture imaginable, and every other architecture can be seen to be its special case, obtained by setting some weights to zeroes.
In a fully connected asymmet network, the connection from one node to asymmetric , another may carry a different weight than the connection from the second node to the first. This architecture is seldom used deposit its generality and conceptual simplicity, due to the large numbers of parameters. In a network there are n2 weights. It is difficult to devise fast learning schemes that can produces fully connected networks that generalize well. It is practically never the case that every node has the direct influence on every other node.
There is no feedback loops i.
Feed-forward ANNs tend to be straight forward networks that associate inputs with outputs. They are extensively used in pattern recognition. This type of organisation is also referred to as bottom-up or top-down. Figure 1. These networks, generally with no more than four layers, are among the most common neural nets in use. Conceptually, nodes in successively higher layers abstract successively higher level features from preceding layers. Feedback networks are very powerful and can get extremely complicated. Feedback networks are dynamic; their 'state' is changing continuously until they reach an equilibrium point.
They remain at the equilibrium point until the input changes and a new equilibrium needs to be found. Feedback architectures are also referred to as interactive or recurrent, although the latter term is often used to denote feedback connections in single-layer organisations. See below Figure The activity of the input units represents the raw information that is fed into the network. The activity of each hidden unit is determined by the activities of the input units and the weights on the connections between the input and the hidden units. The behavior of the output units depends on the activity of the hidden units and the weights between the hidden and output units.
This simple type of network is interesting because the hidden units are free to construct their own representations of the input. The weights between the input and hidden units determine when each hidden unit is active, and so by modifying these weights, a hidden unit can choose what it represents. Figure : An example of a simple feedforward network We also distinguish single-layer and multi-layer architectures.
The singlelayer organization, in which all units are connected to one another, constitutes the most general case and is of more potential computational power than hierarchically AES Institute Of Computer Studies 25 In multi-layer networks, units are often numbered by layer, instead of following a global numbering.
The computation processes in acyclic networks are much simpler than those in networks with exhaustive, cyclic or intra-layer connections. Networks that are not acyclic are referred to as recurrent networks. Modularity allows the neural network developer to solve smaller tasks separately using small modules and then combine these modules in a logical manner.
Keywords: Handwritten Character Recognition, Hidden Markov Models, .. In this thesis, we focus on two major character recognition problems and generate. Optical Character Recognition (OCR) is the process of extracting the characters The author of this thesis tested an Artificial Neural Network (ANN), which is a.
It can be seen as the simplest kind of feedforward neural network: a linear classifier. Its parameters are adapted with an adhoc rule similar to stochastic steepest gradient descent. Because the inner product is a linear operator in the input space, the perceptron can only perfectly classify a set of data for which different classes are linearly separable in the input space, while it often fails completely for non-separable data.
While the development of the algorithm initially generated some enthusiasm, partly because of its apparent relation to biological mechanisms, the later discovery of this inadequacy caused such models to be abandoned until the introduction of non-linear models into the field. The cognitron designed by Kunihiko Fukushima was an early multilayered neural network with a training algorithm.
The actual structure of the network and the methods used to set the interconnection weights change from one neural strategy to another, each with its advantages and disadvantages.
Networks can propagate information in one direction only, or they can bounce back and forth until selfactivation at a node occurs and the network settles on a final state. The parallel distributed processing of the mids became popular under the name connectionism.
The rediscovery of the backpropagation algorithm was probably the main reason behind the repopularisation of neural networks after the publication of "Learning Internal Representations by Error Propagation" in Though backpropagation itself dates from Training was done by a form of stochastic Gradient descent. The employment of the chain rule of differentiation in deriving the appropriate parameter updates results in an algorithm that seems to 'backpropagate errors', hence the nomenclature.
However it is essentially a form of gradient descent. Determining the optimal parameters in a model of this type is not trivial, and steepest gradient descent methods cannot be relied upon to give the solution without a good starting point. In recent times, networks with the same architecture as the backpropagation network are referred to as Multi-Layer Perceptrons. This name does not impose any limitations on the type of algorithm used for learning. The backpropagation network generated much enthusiasm at the time and there was much controversy about whether such learning could be implemented in the brain or not, partly because a mechanism for reverse signalling was not obvious at the time, but most importantly because there was no plausible source for the 'teaching' or 'target' signal.
A few areas where neural networks are currently applied are mentioned below.
Neural networks have broad applicability to real world business problems. In fact, they have already been successfully applied in many industries. Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including: sales forecasting industrial process control customer research data validation risk management target marketing But to give you some more specific examples : ANN are also used in the following specific paradigms: recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from faulty software; interpretation of multimeaning Chinese words; undersea mine AES Institute Of Computer Studies 30 Neural Networks in Medicine Artificial Neural Networks ANN are currently a 'hot' research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years.
At the moment, the research is mostly on modelling parts of the human body and recognizing diseases from various scans e. Neural networks are ideal in recognizing diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks learn by example so the details of how to recognize the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. The quantity of examples is not as important as the 'quantity'. The examples need to be selected very carefully if the system is to perform reliably and efficiently.
Diagnosis can be achieved by building a model of the cardiovascular system of an individual and comparing it with the real time physiological measurements taken from the patient. If this routine is carried out regularly, potential harmful medical conditions can be detected at an early stage and thus make the process of combating the disease much easier.
A model of an individual's cardiovascular system must mimic the relationship among physiological variables i. If a model is adapted to an individual, then it becomes a model of the physical condition of that individual. The simulator will have to be able to adapt to the features of any individual without the supervision of an expert. This calls for a neural network. Sensor fusion enables the ANNs to learn complex relationships among the individual sensor values, which would otherwise be lost if the values were individually analysed.
In medical modelling and diagnosis, this implies that even though each sensor in a set may be sensitive only to a specific physiological variable, ANNs are capable of detecting complex medical conditions by fusing the data from the individual biomedical sensors. Electronic noses have several potential applications in telemedicine.
Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose would identify odours in the remote surgical environment. These identified odours would then be electronically transmitted to another site where an door generation system would recreate them.
Because the sense of smell can be an important sense to the surgeon, telesmell would enhance telepresent surgery.
After training, the net can be presented with input consisting of a set of symptoms; it will then find the full stored pattern that represents the "best" diagnosis and treatment. Almost any neural network application would fit into one business area or financial analysis. There is some potential for using neural networks for business purposes, including resource allocation and scheduling. There is also a strong potential for using neural networks for database mining, that is, searching for patterns implicit within the explicitly stored information in databases.