Handwritten digit recognition using hoeffding tree, decision tree and random forests — A comparative approach

Lavanya, K.; Bajaj, Shaurya; Tank, Prateek; Jain, Shashwat

doi:10.1109/iccids.2017.8272641

Cited by 22 publications

(7 citation statements)

References 7 publications

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“…A total of 240 data points were collected, and seven features in the frequency and time domains are extracted. A decision tree with an accuracy of 82.5% was used for classification purposes because it can easily process non-linear characteristics between values [ 48 ].…”

Section: Related Workmentioning

confidence: 99%

Non-Invasive Driver Drowsiness Detection System

Siddiqui

Saleem

Brown

et al. 2021

Sensors

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Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.

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Section: Related Workmentioning

confidence: 99%

Non-Invasive Driver Drowsiness Detection System

Siddiqui

Saleem

Brown

et al. 2021

Sensors

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“…Further applications of OCR, for supporting the recognition of handwritten digits, are presented in literature. For example in [46], the use of Random Forest, Decision Tree and Hoeffding Tree as classifiers have been proposed to build a tool that can easily recognise the digits. For this, they utilised the handwritten digit dataset of MNIST5, that is preprocessed before being used, in order to extract potential characterising features.…”

Section: Optical Character Recognitionmentioning

confidence: 99%

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

2021

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Internet-based communication systems have become an increasing tool for spreading misinformation and propaganda. Even though there exist mechanisms that are able to track unwarranted information and messages, users made up different ways to avoid their scrutiny and detection. An example is represented by the mixed-code language, that is text written in an unconventional form by combining different languages, symbols, scripts and shapes. It aims to make more difficult the detection of specific content, due to its custom and ever changing appearance, by using special characters to substitute for alphabet letters. Indeed, such substitute combinations of symbols, which tries to resemble the shape of the intended alphabet’s letter, makes it still intuitively readable to humans, however nonsensical to machines. In this context, the paper explores the possibility of identifying propaganda in such mixed-code texts over the Internet, centred on a machine learning based approach. In particular, an algorithm in combination with a deep learning models for character identification is proposed in order to detect and analyse whether an element contains propaganda related content. The overall approach is presented, the results gathered from its experimentation are discussed and the achieved performances are compared with the related works.

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“…For character recognition, the authors have used a pre-trained deep neural network Inception V3 model with two fully connected layers that give a 90% accuracy for broken and faded English characters. For classification (character recognition), researchers have also worked on different machine learning approaches, which include support-vector machine (SVM) [ 45 ], random forests [ 46 ], k-nearest neighbor [ 47 ], decision tree [ 48 ], neural networks [ 49 , 50 , 51 ] etc. These machine learning methods are usually combined with image processing techniques to increase the accuracy of the optical character recognition system.…”

Section: Related Workmentioning

confidence: 99%

iDocChip: A Configurable Hardware Accelerator for an End-to-End Historical Document Image Processing

et al. 2021

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In recent years, there has been an increasing demand to digitize and electronically access historical records. Optical character recognition (OCR) is typically applied to scanned historical archives to transcribe them from document images into machine-readable texts. Many libraries offer special stationary equipment for scanning historical documents. However, to digitize these records without removing them from where they are archived, portable devices that combine scanning and OCR capabilities are required. An existing end-to-end OCR software called anyOCR achieves high recognition accuracy for historical documents. However, it is unsuitable for portable devices, as it exhibits high computational complexity resulting in long runtime and high power consumption. Therefore, we have designed and implemented a configurable hardware-software programmable SoC called iDocChip that makes use of anyOCR techniques to achieve high accuracy. As a low-power and energy-efficient system with real-time capabilities, the iDocChip delivers the required portability. In this paper, we present the hybrid CPU-FPGA architecture of iDocChip along with the optimized software implementations of the anyOCR. We demonstrate our results on multiple platforms with respect to runtime and power consumption. The iDocChip system outperforms the existing anyOCR by 44× while achieving 2201× higher energy efficiency and a 3.8% increase in recognition accuracy.

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Handwritten digit recognition using hoeffding tree, decision tree and random forests — A comparative approach

Cited by 22 publications

References 7 publications

Non-Invasive Driver Drowsiness Detection System

Non-Invasive Driver Drowsiness Detection System

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

iDocChip: A Configurable Hardware Accelerator for an End-to-End Historical Document Image Processing

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