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Artificial Neural Networks (ANNs) are an information processing paradigm that is inspired by biological nervous systems. ANNs are composed of a large number of interconnected processing elements (neurons) working together to solve a specific task. Neural networks learn by example. ANNs are configured for a specific usage, such as pattern recognition or data classification, through a learning process. ANNs, as well as their biological counterparts, learn through the modification of synaptic links between neurons. They are ideal for image to text conversions.
Neural networks appear to be a recent development. This is not true, however; as the idea of making a machine think as a human were their even before computers.
The first artificial neuron was produced in 1943 by the logician Walter Pits and the neurophysiologist Warren McCulloch. But the technology of that time did not allow them to come in full force. Image to text converter download will be invented much later.
Following the initial period of enthusiasm, the field survived a period stagnation and disrepute.
We are to blame two researchers, Minsky and Papert, for the greatest stagnation in the history of neural networks. And for absolutely no reason but their pessimistic view of the future of ANNs, in which, according to them, it was almost impossible to create them. The year was 1969. Since then, for a decade the field was considered dead by the world. But this was not going to last forever.
Currently, the field enjoys a renaissance of interest and a corresponding increase in funding. They are often used image to text converter software.
Neural networks have a remarkable ability to derive meaning from complicated or imprecise data. This ability is used to detect patterns and trends that are too complex for humans or other computer techniques. A trained ANN is considered to be an expert in its task. They are even used to predict future (in a rational meaning). Ever wanted to know how to convert image to text? ANNs is the answer!
Other advantages include:
Adaptive learning: An ability to learn how to do tasks based on the data received from training.
Self-Organisation: ANNs create their own organization or representation of the information they receive during a learning period.
Real Time Operation: ANN computations are carried out in parallel. This means that the time needed to successfully complete a task is drastically decreased. This is most important for software to convert image to text.
Fault Tolerance: Partial destruction of a neural network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.
Conventional computers use an algorithmic approach, i.e. the computer follows a set of commands in order to solve a problem. Unless the specific steps that the computer needs to follow are known the computer cannot solve the problem. That restricts the problem solving capability of conventional computers to problems that are already understood. The algorithmic approach is not very good for image to text apps.
Neural networks handle data the way the brain does. ANNs are composed of a large number of interconnected processing elements working in parallel to solve a specific task. Neural networks learn by example. They cannot be programmed no matter what. The disadvantage is that because ANNs find out how to solve the problem by themselves, their operation cannot be always predicted. Conventional computers are absolutely predictable. If anything goes wrong, this is due to a software or hardware glitch.
ANNs and conventional computers are not in competition. They complement each other. There are tasks that are more suited to an algorithmic approach like arithmetic operations and tasks that are more suited to ANNs. Even more, a large number of tasks require systems that use a combination of the two approaches (normally a conventional computer is used to supervise the neural network) in order to operate at maximum efficiency. This is often used in image to text converter free download.
Feed forward neural networks with backpropagation are the most popular approach to OCR. Text scanner from image software often use this approach.
First, we prepare a training set and train our neural network to recognize patterns from the training set. After the training, we give an arbitrary input to the network and the network forms an output, from which we can resolve a pattern type presented to the network.
Let’s assume that we want to train a network to recognize 26 letters represented as images of 10×10 pixels.
There are two popular different approaches here: the first one uses vectors of the size of our alphabet (26 in this case), the second one uses receptors to “see” the letters.
The first approach (the pixel approach) is good only in scenarios where the input is ALWAYS of the size our neural network’s training program. It WILL NOT work with any other sizes. Most image to text software does not utilize this method.
Receptors are a set of lines with arbitrary size and direction. Any receptor has an activated value if it crosses a letter and deactivated value if it does not cross a letter. The size of an input vector will be the same as receptors count. This allows our neural network to detect letters of any size with great accuracy.
There are some disadvantages, however. The described approach works only with an OCR task. It fails to recognize complex patterns. The most difficult part here is to find the most optimal set of receptors.
The second approach performs OCR image to text conversions better. It also allows to recognize handwritten letters, still the result is not marvelous. More studies are needed to generate receptors more efficiently.
ABBYY has been in the field of neural network since its foundation 25 years ago. They have developed a numerous solutions for various tasks, such as image to text converters, their own dictionary, other text-handling software.
Their most famous product is two-and-a-half-decades old FineReader. It has become so complex that it needs its own article to present its feature set in full color.