Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

Optical character recognition (OCR) is a technology to digitize a paper-based document to digital form. This research studies the extraction of the characters from a Thai vehicle registration certificate via a Google Cloud Vision API and a Tesseract OCR. The recognition performance of both OCR APIs is also examined. The 84 color image files comprised three image sizes/resolutions and five image characteristics. For suitable image type comparison, the greyscale and binary image are converted from color images. Furthermore, the three pre-processing techniques, sharpening, contrast adjustment, and brightness adjustment, are also applied to enhance the quality of image before applying the two OCR APIs. The recognition performance was evaluated in terms of accuracy and readability. The results showed that the Google Cloud Vision API works well for the Thai vehicle registration certificate with an accuracy of 84.43%, whereas the Tesseract OCR showed an accuracy of 47.02%. The highest accuracy came from the color image with 1024×768 px, 300dpi, and using sharpening and brightness adjustment as pre-processing techniques. In terms of readability, the Google Cloud Vision API has more readability than the Tesseract. The proposed conditions facilitate the possibility of the implementation for Thai vehicle registration certificate recognition system.

Analysis of image compression methods based on wavelet transforms for maritime applications

The purpose of the article is to develop recommendations for choosing image compression method based on wavelet transformation, depending on image type, quality and compression requirements. Among the wavelet image compression methods, Embedded Zerotree Wavelet coder (EZW) and Set Partition In Hierarchical Trees (SPIHT) are considered, and the Haar wavelet and wavelet transformation in the oriented basis with the first, third, fifth and seventh decomposition levels is used as the base wavelet transform. These compression methods were compared with each other and with the standard JPEG method on the following parameters: mean square error, maximum error, peak to noise ratio, number of bits per pixel, compression ratio, and image size. The proposed methods can be successfully applied in the transmission of seabed relief images obtained from satellites or sea buoys.

Ontology of Heterogeneous Image File Formats and their Disparate Applications

Different image formats are available in the world today which are used for various purposes, this paper elaborates the Ontology of different Image File Formats and their various applications. Digital images are saved in various Image File Formats which have different properties and features which are ideal for a particular use. A digital image is primarily classified into two types, raster or vector type. Image format elucidate how the information in the image will be stored. Image file format is a systematic way of storing and arranging digital images. Image file format can store data in compressed format (which may be lossy or lossless), uncompressed format or a vector format. Some Image format are suitable for a particular purpose while some are not. TIFF Image type is good for printing whereas PNG or JPG, are best for web. Analysis of the basic Image File Format have been carried out practically and the result is displayed in the coming section

Technology Development Recommendations of Artificial Intelligence used in Library

The library is a place to read books with various collections so that readers can get various sources of knowledge. But in this technological era, people want things that are more practical. With the presence of Artificial intelligence, it can be applied and integrated into the library system. A common problem when readers come to the library is to look for literature according to their choice, both in terms of the name, image, type, and form of the literature. Artificial intelligence can help in searching literature based on recommendations and ratings, so readers don't have to bother looking for the desired literature one by one from the available bookshelves. This certainly makes it easier for readers to search for literature, especially those who are confused about where to look. The recommendation system used is the recommendation method, where the method is a method that combines Filtering and Ranking. This research is intended so that readers who are in the library can easily and quickly search for their literature.

Exploratory Radiomic Analysis of Conventional vs. Quantitative Brain MRI: Toward Automatic Diagnosis of Early Multiple Sclerosis

Conventional magnetic resonance imaging (cMRI) is poorly sensitive to pathological changes related to multiple sclerosis (MS) in normal-appearing white matter (NAWM) and gray matter (GM), with the added difficulty of not being very reproducible. Quantitative MRI (qMRI), on the other hand, attempts to represent the physical properties of tissues, making it an ideal candidate for quantitative medical image analysis or radiomics. We therefore hypothesized that qMRI-based radiomic features have added diagnostic value in MS compared to cMRI. This study investigated the ability of cMRI (T1w) and qMRI features extracted from white matter (WM), NAWM, and GM to distinguish between MS patients (MSP) and healthy control subjects (HCS). We developed exploratory radiomic classification models on a dataset comprising 36 MSP and 36 HCS recruited in CHU Liege, Belgium, acquired with cMRI and qMRI. For each image type and region of interest, qMRI radiomic models for MS diagnosis were developed on a training subset and validated on a testing subset. Radiomic models based on cMRI were developed on the entire training dataset and externally validated on open-source datasets with 167 HCS and 10 MSP. Ranked by region of interest, the best diagnostic performance was achieved in the whole WM. Here the model based on magnetization transfer imaging (a type of qMRI) features yielded a median area under the receiver operating characteristic curve (AUC) of 1.00 in the testing sub-cohort. Ranked by image type, the best performance was achieved by the magnetization transfer models, with median AUCs of 0.79 (0.69–0.90, 90% CI) in NAWM and 0.81 (0.71–0.90) in GM. The external validation of the T1w models yielded an AUC of 0.78 (0.47–1.00) in the whole WM, demonstrating a large 95% CI and a low sensitivity of 0.30 (0.10–0.70). This exploratory study indicates that qMRI radiomics could provide efficient diagnostic information using NAWM and GM analysis in MSP. T1w radiomics could be useful for a fast and automated check of conventional MRI for WM abnormalities once acquisition and reconstruction heterogeneities have been overcome. Further prospective validation is needed, involving more data for better interpretation and generalization of the results.

Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation

Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasting. In recent years, there has been work pertaining to Deep Learning (DL)-based fire segmentation, showing very promising results. However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results. In the present work, we evaluate different combinations of state-of-the-art (SOTA) DL architectures, loss functions, and types of images to identify the parameters most relevant to improve the segmentation results. We benchmark them to identify the top-performing ones and compare them to traditional fire segmentation techniques. Finally, we evaluate if the addition of attention modules on the best performing architecture can further improve the segmentation results. To the best of our knowledge, this is the first work that evaluates the impact of the architecture, loss function, and image type in the performance of DL-based wildfire segmentation models.

The failures of functionalism (for memory)

In Memory: A Self-Referential Account, Fernández offers a functionalist account of the metaphysics of memory, which is portrayed as presenting significant advantages over causal and narrative theories of memory. In this paper, I present a series of challenges for Fernández’s functionalism. There are issues with both the particulars of the account and the use of functionalism more generally. First, in characterizing the mnemonic role of episodic remembering, Fernández fails to make clear how the mental image type that plays this role should be identified. Second, I argue that a functionalist approach, which appeals to the overall structure of the memory system and tendencies of mental state types, is ill-suited to the metaphysical question about episodic remembering that is of interest to the causal and narrative theorists with which Fernandez engages. Fernández’s self-referential account of memory has many other virtues, but functionalism is a poor fit for episodic remembering.

Characteristics of and User Engagement With Antivaping Posts on Instagram: Observational Study (Preprint)

BACKGROUND Although government agencies acknowledge that messages about the adverse health effects of e-cigarette use should be promoted on social media, effectively delivering those health messages is challenging. Instagram is one of the most popular social media platforms among US youth and young adults, and it has been used to educate the public about the potential harm of vaping through antivaping posts. OBJECTIVE We aim to analyze the characteristics of and user engagement with antivaping posts on Instagram to inform future message development and information delivery. METHODS A total of 11,322 Instagram posts were collected from November 18, 2019, to January 2, 2020, by using antivaping hashtags including #novape, #novaping, #stopvaping, #dontvape, #antivaping, #quitvaping, #antivape, #stopjuuling, #dontvapeonthepizza, and #escapethevape. Among those posts, 1025 posts were randomly selected and 500 antivaping posts were further identified by hand coding. The image type, image content, and account type of antivaping posts were hand coded, the text information in the caption was explored by topic modeling, and the user engagement of each category was compared. RESULTS Analyses found that antivaping images of the <i>educational/warning</i> type were the most common (253/500; 50.6%). The average likes of the <i>educational/warning</i> type (15 likes/post) were significantly lower than the <i>catchphrase</i> image type (these emphasized a slogan such as “athletesdontvape” in the image; 32.5 likes/post; <i>P</i>&lt;.001). The majority of the antivaping posts contained the image content element <i>text</i> (n=332, 66.4%), followed by the image content element <i>people/person</i> (n=110, 22%). The images containing <i>people/person</i> elements (32.8 likes/post) had more likes than the images containing other elements (13.8-21.1 likes/post). The captions of the antivaping Instagram posts covered topics including “lung health,” “teen vaping,” “stop vaping,” and “vaping death cases.” Among the 500 antivaping Instagram posts, while most posts were from the <i>antivaping community</i> (n=177, 35.4%) and <i>personal</i> account types (n=182, 36.4%), the <i>antivaping community</i> account type had the highest average number of posts (1.69 posts/account). However, there was no difference in the number of likes among different account types. CONCLUSIONS Multiple features of antivaping Instagram posts may be related to user engagement and perception. This study identified the critical elements associated with high user engagement, which could be used to design antivaping posts to deliver health-related information more efficiently.