Size of Training Set Vis-à-vis Recognition Accuracy of Handwritten Character Recognition System

2013 ◽  
Vol 5 (4) ◽  
Author(s):  
Munish Kumar ◽  
R. K. Sharma ◽  
M. K. Jindal
Keyword(s):  
Character Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Training Set ◽  
Handwritten Character Recognition ◽  
Handwritten Character

Improved handwritten character recognition using second-order information from training set

1993 ◽  
Vol 29 (14) ◽  
pp. 1308 ◽  
Author(s):  
Zs.M. Kovács ◽  
R. Rovatti ◽  
R. Guerrieri ◽  
R. Ragazzoni
Keyword(s):  
Character Recognition ◽  
Second Order ◽  
Order Information ◽  
Training Set ◽  
Handwritten Character Recognition ◽  
Handwritten Character

scholarly journals HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Lei Li ◽  
Xue Gao ◽  
Lianwen Jin
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Mobile Device ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Resource Scheduling ◽  
Computational Resource ◽  
Handwritten Character Recognition ◽  
Performance Loss ◽  
Handwritten Character

Handwritten character recognition (HCR) is a mainstream mobile device input method that has attracted significant research interest. Although previous studies have delivered reasonable recognition accuracy, it remains difficult to directly embed the advanced HCR service into mobile device software and obtain excellent but fast results. Cloud computing is a relatively new online computational resource provider which can satisfy the elastic resource requirements of the advanced HCR service with high-recognition accuracy. However, owing to the delay sensitivity of the character recognition service, the performance loss in the traditional cloud virtualization technology (e.g., kernel-based virtual machine (KVM)) may impair the performance. In addition, the improper computational resource scheduling in cloud computing impairs not only the performance but also the resource utilization. Thus, the HCR online service is required to guarantee the performance and improve the resource utilization of the HCR service in cloud computing. To address these problems, in this paper, we propose an HCR container as a service (HCRCaaS) in cloud computing. We address several key contributions: (1) designing an HCR engine on the basis of deep convolution neutral networks as a demo for an advanced HCR engine with better recognition accuracy, (2) providing an isolated lightweight runtime environment for high performance and easy expansion, and (3) designing a greedy resource scheduling algorithm based on the performance evaluation to optimize the resource utilization under a quality of service (QoS) guaranteeing. Experimental results show that our system not only reduces the performance loss compared with traditional cloud computing under the advanced HCR algorithm but also improves the resource utilization appropriately under the QoS guaranteeing. This study also provides a valuable reference for other related studies.

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.

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