Emergence networks mimics biological nervous system unleash generations of inventions and discoveries in the artificial intelligent field. These networks have been introduced by McCulloch and Pitts and called neural networks. Neural network’s function is based on principle of extracting the uniqueness of patterns through trained machines to understand the extracted knowledge. Indeed, they gain their experiences from collected samples for known classes (patterns). Quick development of neural networks promotes concept of the pattern recognition by proposing intelligent systems such as handwriting recognition, speech recognition and face recognition. In particular, Problem of handwriting recognition has been considered significantly during …show more content…
The first study discusses the basic operations of erosion and dilation and present a system to recognize six handwritten digits. For this purpose, a novel method to recognize cursive and degraded text has been found by Badr & Haralick (1994 ) through using technology of OCR. Parts of symbols (primitives) are detected to interpret the symbols with this method (KUMAR et al. 2010). The study involves mathematical morphology operations to perform the recognition process. Cun et al. (1998) challenge this problem by designing a neural based classifier to discriminate handwritten numeral. This study achieved a reliable system with very high accuracy (over 99%) on the MINIST database. Moreover, the gradient and curvature of the grey character image have been taken into consideration by Shi et al. (2002 ) to enhance the accuracy of handwriting digits recognition. Uniquely, Teow & Loe (2002 ) identify new idea to solve this problem based on biological vision model with excellent results and very low error rate (0.59%). The discoveries and development have been continuing through innovating new algorithms and learning rules. Besides efficiency of using these rules individually in the machine learning, some researchers have going further in developing the accuracy and performance of the learning by mixing several rules to support one
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In this lab, I observed four handwriting exemplars from four different suspects. Roughly a year ago, an unidentified male abducted a ten year old child from a prosperous, private, residential school. The unidentified male then sent a ransom note to the boy’s parents requesting a large amount of money to return their son safely. The authorities identified four male suspects and called me into analyze their handwriting exemplars. I started by using twelve characteristics to analyze the suspects handwriting. These characteristics include line quality, word and margin spacing, continuity of words, ratio of uppercase to lowercase letters, completion of letters, cursive writing, pen pressure, slant, words written on the line or not, curls and loops,
The one feature that got the most attention and strongly influenced its demise was its handwriting recognition capability. In all message pads, handwriting recognition was the basis of data input to many of the built-in applications and functions. According to Professor Luckie, this handwriting recognition depends solely on Paragraph International Inc.’s Calligrapher recognition engine. Calligrapher technology was limited by the dictionary of words to which it has access (Luckie, n.d.). This shows that if you write a word that doesn’t exist in the dictionary, then Calligrapher is not going to recognize it correctly and often suggests funny but incorrect phrases as the user wrote.
Some more advanced methods combine Top-down and bottom-up philosophy. More specificcaly  Et al proposed a new text line location and separation algorithm for complex handwritten documents that is based on the application of a fuzzy directional runlength. Intuitively, the fuzzy runlength at a pixel is how far we can see from standing at the pixel along horizontal direction, by skipping some foreground pixels until a threshold (20 to 25 pixels). A horizontal fuzzy runlength map is generated by scanning every row from left to right, and right to left and keeping the fuzzy runlegth at each pixel. The fuzzy runlength map is then binarized to expose the location of text items that are part of text lines. Finally using a heuristic approach text items are grouped into text lines based on their
Mammography is a popular technique but it has its limitations especially in younger women and in denser breasts. The Computer-Aided-Diagnosis has been proposed for the medical prognosis [7-9]. The fuzzy logic and Artificial Neural Network form the basis of the intelligent systems. There are several instances where the artificial intelligence is used for the diagnosis of the breast cancer. The methods have included many Artificial Neural Networks architectures such as Convolution Neural Network , Radial Basis Network , General Regression Neural Network , Probabilistic Neural Network , Resilient Back propagation Neural Network , and hybrid with Fuzzy Logic . In this paper  a supervised artificial neural network [14-16] was used to help classify the breast lesions into malignant and benign classes by processing the computer cytology images. The accuracy of the trained neural network was found to be 82.21%. The ANN has been established as a robust system for the diagnosis of breast cancer .There is a complex relationship between different biomarkers which were identified for the diagnosis of this cancer , the MLP neural network was simulated for the diagnosis using four biomarkers
The basic principle of this algorithm is to recognize the input paper currency. First of all acquired the image from a particular source. As in this thesis we use for reference images. System read the particular image. Then resize the image. After that the color separator convert the image into RGB to Gray scale and then in binary image. After that the system use color noise median filter. The currency length detector detects the length of the currency. Using the feature extraction techniques the system detect the particular feature of that currency and then the system use pattern matching algorithm to math that particular feature. The input image match with particular database image and according to that we find the currency. In this way this thesis design a automatic system in which we can recognized the paper currency.
The stimulation of putting pen to paper is a hard experience to beat. Even with all of the electronic ways to share ideas that we have today. It is known that writing in cursive is shown to increase brain development. This is especially true in the areas of thinking, and working memory.
For more than thirty years, reaserchers have been working on handwritten recognition. Over the past few years, the number of companies involved in research on handwritten recognition has continually increased. The advance of handwritten processing results from a combination of various elements,
Handwriting analysis is an approach to modeling human activities are to judge the human as a device with a large number of internal mental states. The research builds on the observation that although human behaviors such as speech, handwriting, hand gestures and even American Sign Language. Handwriting work as an indicator, which indicates personality and behavior of humans, for example, recruitment, interviewing and selection, team-building, counseling, and career-planning. The need of human personality recognition is more important in the modern word. Through this human can simplify their jobs and they are able to solve typical problems. Handwriting analysis or Graphology both are the scientific scheme of identifying, recognizing and distinguish
Cursive handwriting and it's relevance in today's grade-school curriculum is not a fresh debate, however with the recent detraction from many schools it begs the question: Is cursive handwriting essential to children's education? Cursive is being sacrificed, as of late, for typing skills in most state school's curriculum. Long thought to provide students with ease in handwritten assignments and note taking, as well as comprehension skills; cursive handwriting is being seen more irrelevant by educators in this modern era. Is writing out a fluid string of words really about improving one's reading and writing abilities? Or are some enthusiasts holding onto what cursive means to them personally?
Cursive writing is a type of writing that was created so you could quickly glide across a page with a writing utensil and write a lot faster than you would be able to when using other forms of writing, but with the creation of typewriters, computers, phones, and other technological advancements such as those, the use of cursive is scarce. When we are learning cursive in school, we could be learning about more important things such as calculus or physics. In my opinion, cursive is not a very useful way of writing and a waste of time when taught in schools.
Over the past few decades, handwriting may seem almost obsolete or those that are under the age of 30. Due to the change of technology over the past 15 years, handwriting seems to suffer. Before the age of technology that bloom through the ages of until today, there were different writing styles that word pairs to use to be successful in the business world end in school. This is before the age of the modern computer and electronic typewriter. This article will help explain why handwriting is important and maybe the future of handwriting as well.
Cursive is a written language that seems to slowly be fading from society. Cursive is still very important in today’s society, and therefore should still be taught to children in elementary school. Even though it may seem that cursive is not important in our technological world today, you may be surprised. Cursive is crucial in learning one’s signature. Children should be able to read cursive because generations before them still use it daily and many historical documents are written in cursive. Also, before we officially do away with cursive, more research should go into how it may help develop young minds, especially those with learning disabilities.
On-line systems for recognizing hand-printed text on the fly have become well-known as commercial products in recent years. Among these are the input devices for personal digital assistants such as those running Palm OS. The Apple Newton pioneered this product. The algorithms used in these devices take advantage of the fact that the order, speed, and direction of individual lines segments at input are known. Also, the user can be retrained to use only specific letter shapes. These methods cannot be used in software that scans paper documents, so accurate recognition of hand-printed documents is still largely an open problem. Accuracy rates of 80% to 90% on neat, clean hand-printed characters can be achieved, but that accuracy rate still translates to dozens of errors per page, making the technology useful only in
We see the number of vehicles around us in our daily life and everyone needs it but with the population increase, vehicles increased last decades in large quantity. But it created disturbances to the human life such as huge traffic, large sound, crime cases accidents, such as stealing of vehicles etc. and therefore we need management of vehicle. As a result, there is a lot of work going on to improve the transportation of vehicles. Out of these, Indian car plate recognition system is the most attractive research issue and this result manuscripts discusses some practical aspect of the recognizing number written on vehicle number plate. A Vehicle Plate Recognition System is a tracking system that identifies the vehicle so that the car is tracked down through our existing database. Each Number plates are unique for each vehicle, its takes the unique identification of each vehicle all over country. So its need to be monitor and recognize. We can track number plates in two ways firstly manually and second is automatically. In India traffic police noted manually the vehicle number plate. It is complex and not accurate reading. But automatic number plate recognition is the fast and efficient based system. Its helps to track the maximum number of system at a time with an accurate reading. Automatic number plate system uses the image