Information-statistical approach to inverse optical problem solution for 3D disperse systems with nano- and micro particles

Multiparameter analysis of simultaneous optical data for 3D disperse systems (consisted from nano- and/or microparticles of different nature) by information-statistical methods can help to estimate the share of different types of particles in mixtures. At the solution of inverse optical problem for unknown poly-component 3D DS, the comparison of measured parameters with the known ones from the set of mono-component 3D DS can help to identify the component content of the system under study. The approach was tested on the biomineral water mixtures of kaolin clay and bacterium coli bacillus with the help of the program based on the information-statistical theory. To solve the impurity optical recognition tasks, the Base of optical data for 3D DS is needed.

Designing of tetrazole conjoined organosilane and organosilatrane via “click approach” using Zn(II) as catalyst: Potent Mycobacterium InhA inhibitor and optical recognition of Fe(III) and Cu(II) ions

Among azoles, tetrazoles are a class of heterocycles that have salient applications in almost every field of science. Organosilicon chemistry is pondering amalgamate of different moieties and has vast applications....

Method of the optical recognition of technical documentation and the transformation of graphic information into machine-readable form for cognitive analysis

The paper proposes the implementation of the method of optical recognition of technical documentation and the transformation of graphic information into a machine-readable form available for cognitive analysis, which is based on the methods of binarization and alignment of images, text segmentation and recognition. The use of the proposed method will provide a dramatic reduction in the costs of cataloging, checking the completeness and inventory of documentation, as well as an increase in design quality due to the semantic analysis of documentation using a knowledge base that is updated automatically. The article presents the development of the algorithm for optical recognition of a document, preparation of an image for optical recognition of a document, an example of the application of the Sauvola method for binarization of an image, and an analysis of the research results. The proposed implementation allows the text recognition on scanned/photographed documents.

Image2SMILES: Transformer-based Molecular Optical Recognition Engine

The rise of deep learning in various scientific and technology areas promotes the development of AI-based tools for information retrieval. Optical recognition of organic structures is a key part of the automated extraction of chemical information. However, this is a challenging task because there is a large variety of representation styles. In this research, we present a Transformer-based artificial neural network to convert images of organic structures to molecular structures. To train the model, we created a comprehensive data generator that stochastically simulates various drawing styles, functional groups, functional group placeholders (R-groups), and visual contamination. We demonstrate that the Transformer-based architecture can gather chemical insights from our generator with almost absolute confidence. That means that, with Transformer, one can fully concentrate on data simulation to build a good recognition model. A web demo of our optical recognition engine is available online at <i>Syntelly</i> platform.

Image2SMILES: Transformer-based Molecular Optical Recognition Engine

The rise of deep learning in various scientific and technology areas promotes the development of AI-based tools for information retrieval. Optical recognition of organic structures is a key part of the automated extraction of chemical information. However, this is a challenging task because there is a large variety of representation styles. In this research, we present a Transformer-based artificial neural network to convert images of organic structures to molecular structures. To train the model, we created a comprehensive data generator that stochastically simulates various drawing styles, functional groups, functional group placeholders (R-groups), and visual contamination. We demonstrate that the Transformer-based architecture can gather chemical insights from our generator with almost absolute confidence. That means that, with Transformer, one can fully concentrate on data simulation to build a good recognition model. A web demo of our optical recognition engine is available online at <i>Syntelly</i> platform.

Combating the hate speech in Thai textual memes

<span>Thai textual memes have been popular in social media, as a form of image information summarization. Unfortunately, many memes contain some hateful content that easily causes the controversy in Thailand. </span><span>For global protection, t</span><span>he </span><em><span>Hateful Memes Challenge</span></em><span> is also provided by </span><em><span>Facebook AI</span></em><span> to enable researchers to compete their algorithms for combating the hate speech on memes as one of </span><em><span>NeurIPS’20</span></em><span> competitions. As well as in Thailand, this paper introduces the Thai textual meme detection as a new research problem in Thai natural language processing (Thai-NLP) that is the settlement of transmission linkage between scene text localization, Thai optical recognition (Thai-OCR) and language understanding. From the results, both regular and irregular text position can be localized by one-stage detection pipeline. More scene text can be augmented by different resolution and rotation. The accuracy of Thai-OCR using convolutional neural network (CNN) can be improved by recurrent neural network (RNN). Since misspelling Thai words are frequently used in social, this paper categorizes them as synonyms to train on multi-task pre-trained language model. </span>