Recognition of Offline Handwritten Characters using 2D-FFT for English and Hindi Scripts

The Handwritten Character Recognition has been a challenging task for the past many decades. This is an old application related to the area of pattern recognition. Handwritten character recognition (HCR) can be classified into two types namely, Online and Offline. As per the literature survey, there are no standard databases for HCR [1] [2] [3] since there are very less number of speakers for any Indian language compared to English. Hence, the database of Indian scripts both for testing and training are to be developed in the laboratory environment. The recognition accuracy for printed / typed characters is more than 99 percent, whereas for the HCR it is around 60 percent. Hence the area of HCR is an open area of research. HCR for Indian languages is at nascent stage compared to English since they contain alphabets and also matra’s / sandhi are complex which make the recognition tougher. The freedom of the scriber in writing the script is also another challenge for achieving the better recognition accuracy. This work describes the handwritten character recognition of both Hindi and English scripts by extracting features using 2D FFT and using the Nearest Neighborhood Classifier. The best recognition accuracy for handwritten character recognition of English and Hindi languages obtained is 70%.

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

Journal of Emerging Technologies in Web Intelligence ◽

10.4304/jetwi.5.4.380-384 ◽

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

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Comparative Study of Handwritten Character Recognition System for Indian Languages

10.1007/978-981-16-4177-0_78 ◽

2021 ◽

pp. 797-806

Author(s):

Meghna B. Patel ◽

Satyen M. Parikh ◽

Ashok R. Patel

Keyword(s):

Comparative Study ◽

Character Recognition ◽

Recognition System ◽

Indian Languages ◽

Handwritten Character Recognition ◽

Handwritten Character

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

Scientific Programming ◽

10.1155/2018/6509275 ◽

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.

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