COMPARISON OF IMAGE SEGMENTATION METHOD IN IMAGE CHARACTER EXTRACTION PREPROCESSING USING OPTICAL CHARACTER RECOGINITON

Today, there are many documents in the form of digital images obtained from various sources which must be able to be processed by a computer automatically. One of the document image processing is text feature extraction using OCR (Optical Character Recognition) technology. However, in many cases OCR technology are unable to read text characters in digital images accurately. This could be due to several factor such as poor image quality or noise. In order to get accurate result, the image must be in a good quality, so that digital image need to be preprocessed. The image preprocessing method used in this study are Otsu Thressholding Binarization, Niblack, and Sauvola methods. While the OCR technology used to extract the character is Tesseract library in Python. The test results show that direct text extraction from the original image gives better results with a character match rate average of 77.27%. Meanwhile, the match rate using the Otsu Thressholding method was 70.27%, the Sauvola method was 69.67%, and the Niblack method was only 35.72%. However, in some cases in this research the Sauvola and Otsu methods give better results

IMPROVED DEEP LEARNING ARCHITECTURE WITH BATCH NORMALIZATION FOR EEG SIGNAL PROCESSING

Deep learning is commonly used to solve problems such as biomedical problems and many other problems. The most common architecture used to solve those problems is Convolutional Neural Network (CNN) architecture. However, CNN may be prone to overfitting, and the convergence may be slow. One of the methods to overcome the overfitting is batch normalization (BN). BN is commonly used after the convolutional layer. In this study, we proposed a further usage of BN in CNN architecture. BN is not only used after the convolutional layer but also used after the fully connected layer. The proposed architecture is tested to detect types of seizures based on EEG signals. The data used are several sessions of recording signals from many patients. Each recording session produces a recorded EEG signal. EEG signal in each session is first passed through a bandpass filter. Then 26 relevant channels are taken, cut every 2 seconds to be labeled the type of epileptic seizure. The truncated signal is concatenated with the truncated signal from other sessions, divided into two datasets, a large dataset, and a small dataset. Each dataset has four types of seizures. Each dataset is equalized using the undersampling technique. Each dataset is then divided into test and train data to be tested using the proposed architecture. The results show the proposed architecture achieves 46.54% accuracy for the large dataset and 93.33% accuracy for the small dataset. In future studies, the batch normalization parameter will be further investigated to reduce overfitting

Emoji captcha; una novedosa opción para proteger sitios web

En el presente trabajo se presenta un desarrollo de una variante de captcha con el uso de una imagen gif, compuesta por 6 fotogramas; cada fotograma está formado por 9 imágenes ordenadas en una matriz de 3 por 3; cada imagen utilizada expresa una emoción de un conjunto de 5 emociones básicas. Cada uno de los 9 sectores en los que se divide el gif, tiene asignado una marca dígito, con el propósito de que el usuario reconozca una emoción dentro del gif y utilizando sus habilidades cognitivas relacione esta emoción con un número y, de esta forma identifique la respuesta correcta. Se propone una alternativa simple y segura para la protección de sitios web, ideal para un amplio rango de usuarios, sin importar su edad o conocimientos informáticos, esto se debe al uso de emociones que son reconocidas por todo ser humano. Los desarrollos actuales realizan análisis de trafico de red y tiempo de respuesta para identificar si el usuario es humano o un programa malicioso, permitiendo a los atacantes acceder a otro tipo de información de usuario de una manera más simple, la ventaja de esta propuesta proviene del hecho de la regeneración del captcha y la redistribución de los números identificadores cada 2 minutos, reforzados por una serie de distorsiones aplicadas, de esta forma los atacantes no pueden acceder a información extra del usuario y debido al tiempo de regeneración lo hace inviable para é

A Review on Human-Computer Interaction and Intelligent Robots

In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research