Hybrid Models for Offline Handwritten Character Recognition System Without Using any Prior Database Images

2017 ◽  
pp. 99-108
Author(s):  
Kamal Hotwani ◽  
Sanjeev Agarwal ◽  
Roshan Paswan
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Hybrid Models ◽  
Handwritten Character Recognition ◽  
Handwritten Character

Handwritten Character Recognition Using Depth Information by Kinect

2013 ◽  
Vol 333-335 ◽  
pp. 883-887
Author(s):  
Yong Xia ◽  
Zhi Bo Yang ◽  
Kuan Quan Wang
Keyword(s):  
Character Recognition ◽  
Recognition Task ◽  
Recognition System ◽  
Depth Image ◽  
Input Device ◽  
Depth Information ◽  
Hand Gestures ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Computer Mouse

t is quite constrained for us to use some other input devices to communicate with computers. In this paper, we integrate human-computer interaction technologies with handwritten Chinese character recognition strategies using depth image information provided by Kinect sensor to realize an unconstrained handwritten character recognition system, which only uses our hand as input device. We predefine several hand gestures as instructions, and for the recognition of these hand gestures, we calculate the contour and fingertips of the hand used for writing using depth image taken by Kinect. By mimicking the functionalities of the computer mouse only using our hands, we can write freely in the air and get the original character image. After Gaussian blurring and normalization, we adopt some classic handwritten character recognition schemes to accomplish the recognition task. Experiments show that the system gives a good result.

scholarly journals Assamese Handwritten Character Recognition using Supervised Fuzzy Logic

2020 ◽  
Vol 8 (5) ◽  
pp. 3750-3758
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Fuzzy Membership Function ◽  
Handwritten Character Recognition ◽  
Fuzzy Classifier ◽  
Uniform Size ◽  
Handwritten Character ◽  
Mean And Variance ◽  
Distance Vector ◽  
Membership Value

This paper presents a state of the art supervised fuzzy pattern recognition system for recognition of Assamese handwritten characters. The fuzzy classifier is well suited for applications with ambiguities and handwritten character recognition is such a task. The dataset used in this experiment is taken from ISI Kolkata. After preprocessing images are normalized into uniform size 42x32 and then two features namely distance vector and density vector have been extracted. The experiment has two stages, training and testing. In first stage we extract distance vector and density features from uniform zones of the binary images for training classes and estimate the mean and variance for each class. In second stage we use this mean and variance to calculate the membership values for each unknown character of the testing set of data. An exponential fuzzy membership function is used for this purpose. Finally we recognize an unknown test character as that class for which it gives highest membership value. Finally result is stored in editable document. The highest recognition accuracy achieved in the experiment is 88.29%, 86.55% and 82.74% for numerals, vowels and consonants respectively.

scholarly journals Handwritten Character Recognition using Deep Learning and Neural Network

2021 ◽  
pp. 371-374
Author(s):  
Nikita Aware ◽  
Ashwini Bhagat ◽  
Komal Ghorpade ◽  
Komal Kerulkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Character Recognition ◽  
Recognition System ◽  
Handwritten Character Recognition ◽  
Query Image ◽  
Research Areas ◽  
Handwritten Character ◽  
Challenging Research ◽  
The Neural Networks

Handwritten character recognition is among the most challenging research areas in pattern recognition and image processing. With everything going digital, applications of handwritten character recognition are emerging in different offices, educational institutes, healthcare units, commercial units and banks etc., where the documents that are handwritten are dealt more frequently. Many researchers have worked with recognition of characters of different languages but there is comparatively less work carried for Devanagari Script. In past few years, however the work carried out in this direction is increasing to a great extent. Handwritten Devanagari Character Recognition is more challenging in comparison to the recognition of the Roman characters. The complexity is mostly due to the presence of a header line known as shirorekha that connects the Devanagari characters to form a word. The presence of this header line makes the segmentation process of characters more difficult. There is uniqueness to the handwriting styles of every individual which adds to the complexity. In this paper, a recognition system based on neural network has been proposed for Devanagari (Marathi) alphabets. Each of the characters that are extracted through query image is resized and is then passed to the neural networks for the process of recognition.

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