An Examination of Character Recognition on ID card using Template Matching Approach

Ryan, Michael; Hanafiah, Novita

doi:10.1016/j.procs.2015.07.534

Cited by 47 publications

(29 citation statements)

References 10 publications

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“…For example, Handwritten character interpretation in Bank cheque processing system or document registration system [24]. Also, such system is important in mail sorting machines [25] or in text to speech conversion system which is very useful for visually impaired people. There are system exists [26] which has higher accuracy as high as 94.65% or 92.14% when the training is more efficient.…”

Section: Related Workmentioning

confidence: 99%

Self-Learning Based Cognitive Reading and Character Recognition in Image Processing Techniques

Kasthuri

Sivaprasatham

2020

SN COMPUT. SCI.

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There are many techniques involved in Handwritten character recognition and many of the methods uses common process like pre-processing, segmentation, stroke identification and character interpretation. But the technique applied for these steps differ from different implementation. Building the training data is a tedious task but it is more important as the success of entire recognition system lies on the amount of training the neural network and building the knowledge base. Hence this is an important task and it is costly due to amount of time and resource used for training the dataset. This article explains some of the methods in Cognitive reading in image processing for character recognition and introduces a self-learning based training system which provides improved method in less time/resource consuming in automatically training the knowledge base.

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Section: Related Workmentioning

confidence: 99%

Self-Learning Based Cognitive Reading and Character Recognition in Image Processing Techniques

Kasthuri

Sivaprasatham

2020

SN COMPUT. SCI.

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“…Template matching is one of the most frequently used techniques in computer vision applications, such as video tracking [35,36,1,9], image mosaicing [25,6], object detection [12,10,34], character recognition [29,2,21,5], and 3D reconstruction [22,23,16]. Classic template matching methods often use sum-of-squared-differences (SSD) or normalized cross correlation (NCC) to calculate a similarity score between the template and the underlying image.…”

Section: Introduction and Reviewmentioning

confidence: 99%

QATM: Quality-Aware Template Matching for Deep Learning

Cheng¹,

Wu²,

AbdAlmageed³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification etc. We propose a novel quality-aware template matching method, QATM, which is not only used as a standalone template matching algorithm, but also a trainable layer that can be easily embedded into any deep neural network. Here, our quality can be interpreted as the distinctiveness of matching pairs. Specifically, we assess the quality of a matching pair using soft-ranking among all matching pairs, and thus different matching scenarios such as 1-to-1, 1-to-many, and many-to-many will be all reflected to different values. Our extensive evaluation on classic template matching benchmarks and deep learning tasks demonstrate the effectiveness of QATM. It not only outperforms state-of-the-art template matching methods when used alone, but also largely improves existing deep network solutions.1 See numpy.tensordot and tensorflow.tensordot.

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“…Typically, a method for recognition of optical characters contain three main phrases: pre-processing, layout analysis and recognition [1], [2]. The pre-processing usually relates to improving the input image to reduce the noise, and to enhance the processing speed in the next phrases.…”

Section: Introductionmentioning

confidence: 99%

A Method for Segmentation of Vietnamese Identification Card Text Fields

Thanh¹,

Nguyen²

2019

IJACSA

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The development of deep learning in computer vision has motivated researches in related fields, including Optical Character Recognition (OCR). Many proposed models and pre-trained models in the literature demonstrate their efficient in optical text recognition. In this context, image processing techniques has an essential role in improving the accuracy of recognition task. Because, depending on the practical application, image text often suffering several degradation from blur, uneven illumination, complex background, perspective distortion and so on. In this paper, we propose a method for pre-processing, text area extraction and segmentation of Vietnamese Identification Card, in order to improve the accuracy of Region of Interest detection. The proposed method was evaluated with a large data set with different practical qualities. Experiment results demonstrate the efficiency of our method.

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An Examination of Character Recognition on ID card using Template Matching Approach

Cited by 47 publications

References 10 publications

Self-Learning Based Cognitive Reading and Character Recognition in Image Processing Techniques

Self-Learning Based Cognitive Reading and Character Recognition in Image Processing Techniques

QATM: Quality-Aware Template Matching for Deep Learning

A Method for Segmentation of Vietnamese Identification Card Text Fields

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