897 research outputs found
Offline Handwriting Recognition Using Genetic Algorithm
In this paper, a new method for offline handwriting recognition is presented. A robust algorithm for
handwriting segmentation has been described here with the help of which individual characters can be
segmented from a word selected from a paragraph of handwritten text image which is given as input to the
module. Then each of the segmented characters are converted into column vectors of 625 values that are later
fed into the advanced neural network setup that has been designed in the form of text files. The networks has
been designed with quadruple layered neural network with 625 input and 26 output neurons each corresponding
to a character from a-z, the outputs of all the four networks is fed into the genetic algorithm which has been
developed using the concepts of correlation, with the help of this the overall network is optimized with the help of
genetic algorithm thus providing us with recognized outputs with great efficiency of 71%
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory,
Tools and Applications (IPTA 2017
On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
On-line handwritten scripts are usually dealt with pen
tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this
paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples
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