A SVM based character recognition system

Sharma, Swapnil; Sasi, Anumol; Cheeran, Alice N.

doi:10.1109/rteict.2017.8256890

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…The primary drawbacks of this strategy are its high cost and lack precision [10]. One research used a time-domain and dynamic feature extraction technique with an 85% accuracy [11]. The support vector machine (SVM) is an excellent method for identifying CAPTCHA characters.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition Of CAPTCHA Characters Using Machine Learning Algorithms

Malhotra¹,

Sandeep²

2022

Artificial Intelligence and Communication Technologies

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The Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is a test used in many online applications to safeguard the authentication process by distinguishing humans from bots. Recognition of characters is incorporated with the help of the CAPTCHA method to make web applications safe and trustworthy. CAPTCHA incorporates some complicated pictures in some cases and owing to noisy values, it might be difficult to distinguish the characters from these images. Using machine-learning approaches, several researchers have attempted to overcome this challenge. As a result, the focus of this work is on a comparison of classification algorithms such as k-NN, SVM, and CNN for recognising CAPTCHA characters in the literature. After a careful review of the past work, it has been determined that CNN, rather than k-NN or SVM, is the most accurate strategy in terms of classification accuracy. In the future, CNN might be used to improve the process of character recognition.

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Section: Literature Reviewmentioning

confidence: 99%

Recognition Of CAPTCHA Characters Using Machine Learning Algorithms

Malhotra¹,

Sandeep²

2022

Artificial Intelligence and Communication Technologies

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“…The identification of letters, numbers, figures such as people is never a simple job for a machine. Many techniques in this area were also proposed [62][63][64][65][66][67][68][69].…”

Section: Applications Of Support Vector Machine (Svm)mentioning

confidence: 99%

“…Text Detection, which has fascinated several researchers [62][63][64][65][66][67][68][69], is another application of SVM. More specifically, Wei et al [64] proposed utilizing two traditional predictive coding machine learning algorithms: logistic regression (LR) and support vector machines (SVM).…”

Section: Literature-based Text Recognition Applicationsmentioning

confidence: 99%

“…Sentimental Analysis refers to Natural Language Processing's role of defining if a text contains subjective information and what information it communicates, i.e., whether it is positive, negative or neutral in the attitude behind the text. Many works [67][68][69][70][71][72][73][74] have therefore been proposed for this purpose. More specifically, Wawre et al [70] Proposed an approach to classifying text feedback on the grounds of the emotions present in such reviews.…”

Section: Literature-based Sentiment Analysis Applicationsmentioning

confidence: 99%

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Machine Learning Applications based on SVM Classification A Review

Abdullah¹,

Abdulazeez²

2021

QAJ

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Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.

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“…1 Traditional digital recognition methods include template matching [11,12], structural feature [13,14] and support vector machine (SVM) [15,16]. The template matching method is to first establish a template for each character and then use the template to compare with the character to be recognized.…”

Section: Related Workmentioning

confidence: 99%

Fast identification method for express end sorting label code based on convolutional recurrent neural network

et al. 2020

SIViP

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In the automatic sorting process of express, the express end sorting label code is used to indicate that the express is dispatched to a specific address by a specific courier. Since there are many areas on the express bill containing digital information, some areas may be improperly photographed, etc. The difficulty in positioning and recognizing the express end sorting label code region is increased. To solve this problem, this paper proposes an express end sorting label code recognition method with convolutional recurrent neural network for the code specification, which has certain versatility. In order to improve the overall code recognition speed, this paper optimizes the traditional digital recognition method, removes the original segmentation operation of the character and recognizes the code as sequence recognition. Firstly, the coding region is located, and then, the express end sorting label code is recognized by the convolutional recurrent neural network. In order to test the experimental performance, this paper tests on Free-Type dataset and SUN-synthesized dataset. The experimental results show that the proposed method improves the recognition accuracy and processing speed of the express end sorting label code.

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A SVM based character recognition system

Cited by 15 publications

References 13 publications

Recognition Of CAPTCHA Characters Using Machine Learning Algorithms

Recognition Of CAPTCHA Characters Using Machine Learning Algorithms

Machine Learning Applications based on SVM Classification A Review

Fast identification method for express end sorting label code based on convolutional recurrent neural network

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