Application Of A Handwriting From A

Optical character recognition is the method or process of detection and translation of the printed or written document into an electronic data document and is usually used in data processing systems. Such recognitions requires definite hardware and software tools which can translate the data. [19]

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,

In regards to writing I have always struggled to meet all of the requirements for a perfect paper that includes a well-organized text filled with rich analytical sentences. In the beginning of the semester I constantly worried about the structure of my writing and its overall material. In high school it was important for me to complete my essays by answering the questions that I was given, but in college, students go more into depth. College students are required to focus on grammar, and well-structured ideas that flow along with rich analytical content. After I was given my first assignment I was pressured to improve my overall work by focusing on structure and developing analytical content. Assignments such as the weekly readings and writing assignments helped me improve my overall work. Through the narrative essay, artist research essay and Storify project I focused on many aspects that improved my writing. For all of my assignments it was useful to use techniques, such as, reading out loud to revise the structure of my work and going into depth with description.

He didn't want to be here. This was the third time that John ended up getting called into the office. John sighed inwardly. The time had flown so fast, he didn't realize that his thesis paper was due. The night before, he called all his friends hoping that someone could help him, but it was to no avail. After swallowing, he steadied his nerves, and knocked on the dean's looming, large, rosewood door.

Visually impaired people face a lot of problems in their daily life. In order to make them live confidentially without the need of others we develop an assistive technology. We propose a camera based tool which helps to read the text label from the products that are commonly in use. We first propose image capturing method that is usually a camera used to capture the object whose label is to be read. Then feature extraction, every image has its own features different from others. We use certain feature extraction algorithms like gradient conversion, sobel edge detection, canny edge detection etc. Histogram might be applied to extract the image features. Finally speech output the processed image after recognition label text is output to visually impaired people as speech output. Raspberry pi is used for this entire application.

Feature selection (FS) methods have been used in the since 70s, using in the fields of statistics and pattern recognition. Pattern recognition system is one of the most important and indispensable tasks in overcome the curse of dimensionality problem, which forms a motivation for using a suitable feature selection method. According to their working principles, there are two types of methods are using in feature selection: methods which select the best subset of features that has a certain number of features And methods which select the best subset of features according to their own principles, independent of outside size measures [base].

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

Tested for a number of datasets and it is found that accuracy of image recognition for proposed approach is higher. The results are evaluated for different resolutions of the image and it is found that even for low resolution accuracy is considerably more for proposed approach.

Technology is an evolving tool with many purposes. Its usefulness carries over from fields like Information Technology (IT) to other business fields like accounting or human resources. Technology can help a business become more efficient and have more accurate information. It can also help with data analysis to create more accurate and useful reports for stockholders and upper levels of management to make decisions about a company. Every year technology continues to change and new technology and software trends emerge. Over the past decade Optical Character Recognition has become an emerging technology and is now being used in many fields to aid businesses and business processes.

Due to the rapid progress in digital cameras industry, camera captured documents becomes as another possibility or choice for document capturing and understanding for OCR applications. However, image quality degradations arising from the image acquisition process have severe effect on these applications. The distortion results from digital camera may take the shape of skew, perspective distortion or geometric distortion. We propose fully automatic preprocessing techniques to enhance the digital camera captured images to improve the performance of OCR applications. Our algorithms depend on the features of the text lines and letters and do not need any especial equipment. Experimental results on a real camera captured images demonstrate 10~15 % enhancement in current commercial OCR packages.

The segmented regions of the image are then screened to identify plausible characters. First, the module checks simply the size and area constraints

Keywords: Online handwriting recognition, online character recognition, lexicon driven approach, Bangla script, Indic script, cursive text recognition.

The linearity in the network of neural network is the most attention seeking cause of using it. A single layer adaptive network was firstly used for the face recognition which is also known as WISARD. For each provided individual, it has a single network. For an effective recognition, the construction of the neural network structure is very important which are based on the applications we had studied before. Different types of neural network is used for different techniques, for example, multilayer perceptron shown in fig.2.1 is used for the detection of the face and multi-resolution pyramid structure shown in fig.2.2 is used for the verification of the face.

Optical character recognition (OCR) is the process of converting the text of a document image into a format that can be easily edited by a computer. It is widely used as a form of data entry from printed paper data records, whether passport documents, invoices, bank statement, receipts, business card, mail, or other documents. There are many commercial OCR systems available, however, most of the time, poor quality printed documents yield low recognition rates when submitted to them.

1.1.4 Optical Character Recognition [b]: Optical Character Recognition is a process mechanical or electronic conversion of scanned or photographic image of typewritten or printed text into machine encode or computer readable text.