Text Extraction Research Articles

Remote supervised relationship extraction method of clustering for knowledge graph in aviation field

In the process of building domain knowledge graph, the result of relationship extraction between entities is an important guarantee of the quality of the graph. Therefore, we propose a clustering method based on reinforcement learning for remote supervised relation extraction. For the relationship extraction of accident information in the aviation domain mapping, a clustering method combining local dense and global dissimilarity is proposed in combination with remote supervision, which can obtain a large amount of low-noise labeled data and reduce part of the wrong labeling and missing labeling due to the strong specialization in the aviation domain; meanwhile, reinforcement learning is introduced to denoise the negative instance noise in the positive sample data; Finally, we propose a two-attention segmentation (DAPCNN) relationship extraction model to mine deep semantic sentences. The experimental results show that in the civil aviation relationship extraction text constructed in this paper, the Micro_R, Micro_P and Micro_F1 values of the proposed relationship extraction method reach 83.41 %, 84.16 % and 83.96 %. In the open relationship extraction dataset DuIE, The Micro_R, Micro_P and Micro_F1 of the proposed method are up to 83.41 %, 93.58 % and 94.02 % respectively. Compared with the current advanced multi-instance and multi-label model, the proposed method can more accurately extract the relationship between aviation accident entities. At the same time, the performance of the open data set is also good, and has a certain universality.

Extractive Arabic Text Summarization Using PageRank and Word Embedding

Research on graph-based automatic text summarization for Arabic, the official language of 26 nations with over 200 million speakers, as well as other prevalent languages, has recently increased due to the ability of these approaches to handle linguistic peculiarities such as complex morphological linkages. The present paper proposes a graph-based extractive Arabic text summarization (GEATS) technique that employs word embedding and PageRank algorithms for feature extraction and sentence ordering. The efficiency of the GEATS approach versus the state-of-the-art methods is analyzed based on the quality of the produced summaries over the F-measure values. The findings indicated that it outperformed the nearest alternative by an advantage of over 7.5%.

A text extraction framework of financial report in traditional format with OpenCV

Due to intensified off-balance sheet disclosure by regulatory authorities, financial reports now contain a substantial amount of information beyond the financial statements. Consequently, the length of footnotes in financial reports exceeds that of the financial statements. This poses a novel challenge for regulators and users of financial reports in efficiently managing this information. Financial reports, with their clear structure, encompass abundant structured information applicable to information extraction, automatic summarization, and information retrieval. Extracting headings and paragraph content from financial reports enables the acquisition of the annual report text’s framework. This paper focuses on extracting the structural framework of annual report texts and introduces an OpenCV-based method for text framework extraction using computer vision. The proposed method employs morphological image dilation to distinguish headings from the main body of the text. Moreover, this paper combines the proposed method with a traditional, rule-based extraction method that exploits the characteristic features of numbers and symbols at the beginning of headings. This combination results in an optimized framework extraction method, producing a more concise text framework.

PODCAST TRANSCRIPTION AND SUMMARIZATION WITH SPEECH SYNTHESIS

We propose a system which automatically generate audio summaries for podcasts, allowing listeners to quickly preview podcast episodes. The proposed system first transcribes the audio from a podcast using automatic speech recognition (ASR), then summarizes the transcript using extractive text summarization, and finally returns the audio associated with the text summary. Motivated by a lack of relevant datasets for this task, we created our own by transcribing the audio from various podcasts and generating summaries for these transcripts using a manual annotation tool. After transcription, the text is processed through natural language processing (NLP) algorithms to extract the most important points, key topics, and relevant information from the podcast. The first component of the system involves automatic transcription, where speech-to-text algorithms convert audio podcast episodes into textual format. This transcription process employs state-of-the-art machine learning models trained on large datasets to achieve high accuracy and reliability. The system employs natural language processing (NLP) techniques to generate concise summaries of podcast episodes. These summaries aim to capture the essence of the content, providing users with a quick overview of key topics, insights, and discussions covered in the episode. Key Words: Podcasts, audio summarization, automatic speech recognition, automatic summarization.

“With regard to the last article in the volume…”– A note on Rush Rhees and “The Study of Philosophy” in Without Answers

Based on material from Rush Rhees’ Nachlass, this article reconstructs, in PART I, the circumstances that motivated Rhees to include “The Study of Philosophy” as the concluding chapter of his 1969 publication Without Answers. As originally conceived, this chapter was longer than the version that eventually appeared in print. The reconstruction references the correspondence between Rhees and the editor of Without Answers, Dewi Z. Phillips. It outlines the central ideas of “The Study of Philosophy”, including Rhees’ clarifications of Wittgenstein’s call to “Go the bloody hard way”. The original, somewhat longer version of the chapter is reproduced in PART II of the article. It consists of two text extracts from two letters to Maurice O’C. Drury from July and September 1963. Drury’s “intermediate” letter to Rhees from August 1963 is also reproduced. This article is also a “narrative” about the way one of Wittgenstein’s editor’s experiences being edited and published via an editor.

Enhancing Study Experience using Handwritten Character and Digit Recognition and Text Summarization

The integration of Handwritten characters and, Digit Recognition and Deep Learning in education heralds a transformative era in learning methodologies. This abstract delves into the multifaceted benefits derived from the amalgamation of these technologies, redefining the educational landscape. Handwritten characters and Digit Recognition technology facilitates the seamless digitization of handwritten content, transcending the limitations of manual note-taking. Its introduction into educational frameworks enhances accessibility, promotes organization, and augments the searchability of diverse educational materials. Deep Learning, acting in tandem with Handwriting Recognition, amplifies these advantages manifold. Deep learning powered study assistants offer personalized learning experiences tailored to individual needs, adapting to varied learning styles. Additionally, these assistants facilitate collaborative opportunities, providing real time feedback and evaluation tools that revolutionize the learning process. Key Words: Image recognition, CNN, RNN, LSTM, Neural Networks, SVM, Deep Learning, Convolutional layer, PreLU, ReLU, Text Summarization, Extractive text summarization, Tokenization.

Complexity Analysis of Chinese Text Based on the Construction Grammar Theory and Deep Learning

Due to the complexity of Chinese and the differences between Chinese and English, the application of Chinese text in the digital field has a certain complexity. Taking Chinese text in Open Relation Extraction (ORE) as the research object, the complexity of Chinese text is analyzed. An extraction system of word vectors based on construction grammar theory and Deep Learning (DL) is constructed to achieve smooth extraction of Chinese text. The work of this paper mainly includes the following aspects. To study the application of DL in the complexity analysis of Chinese text based on construction grammar, firstly, the connotation of construction grammar and its role in Chinese text analysis are explored. Secondly, from the perspective of the ORE of word vectors in language analysis, an ORE model based on word vectors is implemented. Moreover, an extraction method based on the distance of word vectors is proposed. The test results show that the F1 value of the proposed algorithm is 67% on the public WEB-500 and NYT-500 datasets, which is superior to other similar text extraction algorithms. When the recall rate is more than 30%, the accuracy of the proposed method is higher than several other latest language analysis systems. This indicates that the proposed Chinese text extraction system based on the DL algorithm and construction grammar theory has advantages in complexity analysis and can provide a new research idea for Chinese text analysis.

Research on Sentiment Analysis of Micro-blog based on Attention-BiLSTM

The rapid development of the Internet gives birth to massive data. These data have the characteristics of multiple types, low density, and fast transmission speed. As one of the products of the era of big data, micro-blog provides users with a highly interactive platform. In order to explore the value and sentiment contained in micro-blog comment texts, and solve the problem of semantic loss and over-reliance on manual work in traditional text sentiment analysis, this paper proposes a deep learning model based on Attention-BiLSTM. In the embedding layer, the CBOW method of Word2vec is used to transform the text data into word vectors, and the attention mechanism is integrated to dynamically weight the input word vectors. Secondly, BiLSTM is used to extract text features, then fuse them in concat layer. After that the classification is realized by Softmax whose results of the output text are positive or negative. Finally, by comparing with TextCNN, LSTM and BiLSTM, the result shows that the Attention-BiLSTM model does have better classification effect, strong generalization and practicability.

Expression of Concern: Oil painting color image enhancement recognition method based on artificial intelligence: applications of an AI model in environmental research

ABSTRACT Due to the pollution of the air and water environment and the problem of forgery, it is difficult to identify oil paintings. The reason is that air pollution and water pollution can lead to moisture, mold, and even water stains on the picture, which will seriously damage the integrity and color performance of the picture. At the same time, chemicals in the water may also have a corrosive effect on the oil painting, further destroying the color and detail of the picture. The problem of relying entirely on the conventional experience of experts is too subjective. Some controversial works are difficult to convince people with rational identification evidence, so it is necessary to explore a scientific and effective method to quantify the authenticity of oil paintings. This paper constructs an oil painting authenticity identification method based on multi-feature fusion based on the artistic style analysis and feature extraction of oil painting shape, color and texture. The recognition accuracy of the proposed method is compared with that of the existing neural network. The results show that the recognition rate of the proposed model is 73.0%, which is the best performance.

Enhancing Lip Reading: A Deep Learning Approach with CNN and RNN Integration

This research introduces an innovative approach to enhance lip reading-based text extraction and translation through the integration of a double Convolutional Neural Network (CNN) coupled with Recurrent Neural Network (RNN) architecture. The proposed model aims to leverage the strengths of both CNN and RNN to achieve superior accuracy in lip movement interpretation and subsequent text extraction. The methodology involves training the double CNN+RNN model on extensive datasets containing synchronized lip movements and corresponding linguistic expressions. The initial layers of the model utilize CNNs to effectively capture spatial features from the visual input of lip images. The extracted features are then fed into RNN layers, allowing the model to grasp temporal dependencies and contextual information crucial for accurate lip reading. The trained model showcases its proficiency in extracting textual content from spoken words, demonstrating an advanced capability to decipher nuances in lip gestures. Furthermore, the extracted text undergoes a translation process, enabling the conversion of spoken language into various target languages. This research not only contributes to the advancement of lip reading technologies but also establishes a robust foundation for real-world applications such as accessibility solutions for individuals with hearing impairments, real-time multilingual translation services, and improved communication in challenging acoustic environments. The abstract concludes with a discussion on the potential impact of the double CNN+RNN model in pushing the boundaries of human-computer interaction, emphasizing the synergy between deep learning, lip reading, and translation technologies

Visionary Audio Hub using Machine Learning

Image-to-text-to-speech conversion, powered by machine learning, is an emerging field to transform how we access and engage with information. By integrating optical character recognition (OCR) and text-to-speech (TTS) technologies, machine learning enables the extraction of text from images and its conversion into speech with greater accuracy and efficiency than ever before. This technology holds immense potential to enhance accessibility for a broad spectrum of users, including those with visual impairments, students, tourists, researchers, and musicians. For instance, individuals with visual impairments can use image-to-text-to-speech conversion to access scanned textbooks and course materials in speech format, facilitating easier comprehension and study. Similarly, tourists can leverage this technology to translate foreign language signs and text into speech, aiding navigation in unfamiliar environments. Researchers can benefit from image-to-text-to-speech conversion by extracting data from scientific papers and documents, simplifying analysis and synthesis processes. Moreover, musicians can explore new creative avenues by converting text to speech and manipulating the audio output to innovate musical compositions. Machine learning algorithms play a crucial role in improving the quality and naturalness of synthesized speech in these systems. By considering factors such as language, accent, and prosody, machine learning algorithms generate speech that closely resembles human speech patterns, enhancing clarity and understanding. Keywords— Image-to-text-to-speech conversion, Machine learning, Optical character recognition (OCR), Text-to-speech (TTS) technologies, Accessibility, Naturalness of synthesized speech.

A custom-built deep learning approach for text extraction from identity card images

<span>Information found on an identity card is needed for different essential tasks and manually extracting this information is time consuming, resource exhaustive and may be prone to human error. In this study, an optical character recognition (OCR) approach using deep learning techniques is proposed to automatically extract text related information from the image of an identity card in view of developing an automated client onboarding system. The OCR problem is divided into two main parts. Firstly, a custom-built image segmentation model, based on the U-net architecture, is used to <span>detect the location of the text to be extracted. Secondly, using the location of the identified text fields, a (CRNN) based on long short-term memory (LSTM) cells is trained to recognise the characters and build words. Experimental results, based on the national identity card of the Republic of Mauritius, demonstrate that our approach achieves higher accuracy compared to other studies. Our text detection module has an intersection over union (IOU) measure of 0.70 with a pixel accuracy of 98% for text detection and the text recognition module achieved a mean word recognition accuracy of around 97% on main fields of the identity card.</span></span>

Candidate Authentication using OCR Techniques

This paper explores the application of Optical Character Recognition (OCR) techniques for authenticating candidates based on identity documents. As advancements in technology continue to redefine various industries, the integration of OCR into candidate authentication processes offers a streamlined and efficient solution. The aim is to design a system which gets the image of the identity proof and the details are being retrieved using the character segmentation which is done by a feature extraction optical character recognition algorithm (OCR). The authentication process encompasses document validation and implementation of security measures to safeguard against fraud.By combining YOLO object detection, Optical Character Recognition (OCR) and fuzzy matching technique ,this approach aims to enhance the accuracy and reliability of candidate authentication. This underscores the importance of a comprehensive authentication framework, incorporating both technological and procedural elements, to establish a robust and secure candidate authentication process in diverse contexts. Keywords: ID Verification,Optical Character Recognition (OCR), Text Extraction, YOLO,Object Detection,Image Segmentation, EasyOCR , Fuzzy Matching.

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

Automated Fabric Defect (FD) detection plays a crucial role in industrial automation within fabric production. Traditionally, the identification of FDs heavily relies on manual assessment, facilitating prompt repairs of minor defects. However, the efficiency of manual recognition diminishes significantly as labor working hours increase. Consequently, there is a pressing need to introduce an automated analysis method for FD recognition to reduce labor costs, minimize errors, and improve fabric quality. Many researchers have devised defect detection systems utilizing Machine Learning (ML) approaches, enabling swift, accurate, and efficient identification of defects. This study presents the Optimal Artificial Neural Network-based Fabric Defect Detection and Classification (OANN-FDDC) technique. The OANN-FDDC technique exploits handcrafted features with a parameter-tuning strategy for effectively detecting the FD process. To obtain this, the OANN-FDDC technique employs CLAHE and Bilateral Filtering (BF) model-based contrast augmentation and noise removal. Besides, the OANN-FDDC technique extracts shape, texture, and color features. For FD detection, the ANN method is utilized. To improve the detection results of the ANN method, the Root Mean Square Propagation (RMSProp) optimization technique is used for the parameter selection process. The simulation outputs of the OANN-FDDC technique were examined on an open fabric image database. The experimental results of the OANN-FDDC technique implied a better outcome than the 96.97% of other recent approaches.

Highly efficient recognition of similar objects based on ionic robotic tactile sensors

Tactile sensing provides robots the ability of object recognition, fine operation, natural interaction, etc. However, in the actual scenario, robotic tactile recognition of similar objects still faces difficulties such as low efficiency and accuracy, resulting from a lack of high-performance sensors and intelligent recognition algorithms. In this paper, a flexible sensor combining a pyramidal microstructure with a gradient conformal ionic gel coating was demonstrated, exhibiting excellent signal-to-noise ratio (48 dB), low detection limit (1 Pa), high sensitivity (92.96 kPa−1), fast response time (55 ms), and outstanding stability over 15,000 compression-release cycles. Furthermore, a Pressure-Slip Dual-Branch Convolutional Neural Network (PSNet) architecture was proposed to separately extract hardness and texture features and perform feature fusion. In tactile experiments on different kinds of leaves, a recognition rate of 97.16% was achieved, and surpassed that of human hands recognition (72.5%). These researches showed the great potential in a broad application in bionic robots, intelligent prostheses, and precise human–computer interaction.

A multimodal approach using fundus images and text meta-data in a machine learning classifier with embeddings to predict years with self-reported diabetes – An exploratory analysis

AimsMachine learning models can use image and text data to predict the number of years since diabetes diagnosis; such model can be applied to new patients to predict, approximately, how long the new patient may have lived with diabetes unknowingly. We aimed to develop a model to predict self-reported diabetes duration. MethodsWe used the Brazilian Multilabel Ophthalmological Dataset. Unit of analysis was the fundus image and its meta-data, regardless of the patient. We included people 40 + years and fundus images without diabetic retinopathy. Fundus images and meta-data (sex, age, comorbidities and taking insulin) were passed to the MedCLIP model to extract the embedding representation. The embedding representation was passed to an Extra Tree Classifier to predict: 0–4, 5–9, 10–14 and 15 + years with self-reported diabetes. ResultsThere were 988 images from 563 people (mean age = 67 years; 64 % were women). Overall, the F1 score was 57 %. The group 15 + years of self-reported diabetes had the highest precision (64 %) and F1 score (63 %), while the highest recall (69 %) was observed in the group 0–4 years. The proportion of correctly classified observations was 55 % for the group 0–4 years, 51 % for 5–9 years, 58 % for 10–14 years, and 64 % for 15 + years with self-reported diabetes. ConclusionsThe machine learning model had acceptable accuracy and F1 score, and correctly classified more than half of the patients according to diabetes duration. Using large foundational models to extract image and text embeddings seems a feasible and efficient approach to predict years living with self-reported diabetes.

Research on Visual Inspection Method of Tree Whitening Quality based on Multi-level Feature Fusion

Background: At present, the traditional manual whitewashing method is used in tree whitewashing in China. The quality of tree whitening can only be judged by the naked eye. Objective: Up to now, the quality of tree whitewashing is still judged manually. In order to improve work efficiency, an automatic evaluation method of tree whitewashing quality based on multi-level feature fusion is proposed. Methods: The images extract texture features from white-washed trees (gray-level co-occurrence matrix) and shape features (gradient direction histogram) from pixel-level fusion to obtain a global characteristic matrix. Using a support vector machine (SVM), random forests, and clustering algorithm (KNN), three classifiers were selected to identify the integration characteristics of the training test. In order to reduce the correlation of feature information and improve the execution efficiency of the classifier, the global feature matrix was optimized by combining principal component analysis (PCA) with pixel-level fusion and feature-level fusion. Results: The experiment results showed that the classification accuracy of the support vector machine is 94.00%, which is higher than that of the random forest classifier (92.67) and KNN classifier (92.67%). Meanwhile, the support vector machine is superior to the other two classification algorithms in recall rate, accuracy rate, and algorithm execution efficiency. The results showed that the execution efficiency of each classification algorithm was improved after the optimization of the feature data. The support vector machine classification algorithm is more stable than the other two algorithms. Conclusion: The feature fusion method combined with PCA can improve the execution efficiency and recognition precision of classifiers to a certain extent. For the feature matrix obtained by different data processing methods, the SVM classifier performs more stably and reliably than the random forest classifier and KNN in the inspection of tree whitening quality.

Development of an Intelligent Imaging System for Determining Maturity of Copra Flesh in Coconuts Using Shape and Texture Extraction

Copra is dried coconut meat that is used to produce coconut oil. According to the Central Statistics Agency (BPS), Indonesia's copra production in 2020 reached 2.3 million tonnes. This is one form of the process of improving the economy of people living on the coast. This research was conducted to educate farmers in determining the level of maturity of the copra meat produced. This research was conducted using an extraction method that involves colour extraction and texture extraction. the method is used to provide convenience in seeing the level of maturity of the two characteristics of copra obtained in the field, namely texture and colour. The process obtained in the training with one of the images used as a test image in colour extraction produces area, perimeter, metric and eccentricity values in label 3 with values of 651.00, 184.69, 0.24 and 0.89. while in the feature extraction method the results are obtained with an average intensity value of 243.31, standard deviation of intensity 39.76 and entropy value of the tested image 4.57. The method is able to perform a detection process so that it can determine the level of maturity of copra seen from the existing types of copra such as asalan copra, regular copra, black copra and wet copra, each of which provides different functions in the copra processing stage. The process will be carried out using KNN which is seen from all test data and training data stored after the detection process. The results of the process carried out using digital images involving the extraction method for detection and KNN for classification are able to provide the right value. This is evidenced by the better accuracy value of 98%.

Mining and Teaching Practice of Ideological and Political Course Elements Based on Text Extraction and Knowledge Graph

With the rapid development of the country’s economy, politics, and culture, China has swiftly ascended to the ranks of global powers. Its participation in international organizations, including the WTO, has significantly bolstered its global standing and diplomatic ties, making it an indispensable player in international politics. Meanwhile, domestically, China has implemented numerous initiatives aimed at improving the lives of its citizens, such as anti-corruption campaigns, efforts to uphold integrity, crackdowns on criminal organizations, and poverty alleviation programs. As a result, the well-being of the populace has seen a steady increase. Furthermore, China has embarked on a new era of education characterized by its unique attributes, with civic education platforms experiencing comprehensive development. This paper examines these developments through text and knowledge mapping, assessing the efficacy of this approach within the framework of course ideology and politics.

Recognition performance of different artificial neural networks for distinguishing banana slices subjected to different combinations of pretreatment and microwave drying

This study was aimed at assessing the effect of microwave drying at 100, 200, or 300 W on the quality of Cavendish banana slices without pretreatment and with pretreatment using 5% ascorbic acid solution, 5% citric acid solution, 5% gum arabic solution, and ultrasound. Banana slices were imaged using a digital single-lens reflex (SLR) camera. The acquired images were processed to extract texture parameters. The classification models were developed based on image texture parameters selected from a big dataset of 2172 textures of images in different color channels using artificial neural networks. Wide Neural Network, Bilayered Neural Network, Medium Neural Network and three classifiers from the group of function, such as RBF (Radial basis function) Network, Multilayer Perceptron, and WiSARD were applied. Banana slices belonging to 15 classes with different combinations of pretreatment and microwave drying were distinguished with an average accuracy of up to 97.2% for a model built using Multilayer Perceptron. For most models, banana samples microwave-dried at 200 W without pretreatment were classified with the highest correctness. The performed study revealed that the objective, non-destructive, correct, and robust quality assessment of pretreated and microwave-dried banana slices may be performed using image processing and artificial intelligence.