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This study uses Word2Vec's word vector representation technology to finely capture the semantic relationships of vocabulary in legal texts through the Skip-gram model. By introducing Hierarchical Softmax optimization, a legal privacy protection model ...

Industrial digitalization serves as a pivotal factor in enhancing efficiency and minimizing costs. However, industrial design drawings, which are indispensable for conveying product information, pose significant challenges to computer vision systems due ...

This study focuses on improving the optical character recognition (OCR) data for panels in COMICS [18], the largest dataset containing text and images from comic books. To do this, we developed a pipeline for OCR processing and labeling of comic ...

In this paper, we investigate self-supervised pre-training methods for document text recognition. Nowadays, large unlabeled datasets can be collected for many research tasks, including text recognition, but it is costly to annotate them. Therefore,...

We present a text and imaging dataset of Byzantine-era Medieval Greek inscriptions, suitable as a challenging testbed for Computer Vision and Natural Language Processing tasks. The lack of sizable related training sets, as well as difficulties ...

Text Recognition (TR) technology leverages a range of deep learning techniques to analyze and identify characters and words embedded in images. Its scope encompasses handwritten, printed, and scene text recognition. In this paper, we take a ...

Professional Training for laser powder bed fusion equipment operation typically is time consuming and expensive for end-users in metal Additive Manufacturing (AM). This study proposes a practical solution by leveraging Hyperskill software to ...

This article proposes a multisource heterogeneous experimental data recognition method based on CRNN and DBNet in a cloud-edge collaborative environment in an attempt to address the issues of low efficiency and a high error rate that come with ...

This paper presents CALEND_AR, a hybrid calendar application that uses augmented reality technologies to blend physical and digital calendars. The study investigates user preferences and motivations for using physical or digital calendars and ...

Most text recognition methods are trained on large amounts of labeled data. Although text images are easily accessible, labeling them is costly. Thus how to utilize the unlabeled data is worth studying. In this paper, we propose a MUltiple ...

As a task aiming to recognize text from images, text recognition is of great significance in both industry and academia. The vast majority of existing text recognition methods use text images with the same styles as training and testing samples. ...

Character recognition (CR) has been the subject of substantial research over the past half century and has now reached a level of development that is adequate to build applications driven by technology. Now, the fast expanding computer power makes it ...

With the large-scale construction of new substations and the intelligent upgrading of existing substations, higher requirements have been put forward for efficient and intelligent maintenance of substations. In practical applications, the substation ...

The emerging 5G telecommunication technology uses novel aspects to fulfill the challenges of high data rate, ultra-low latency, broad bandwidth with the best user experience for text detection in sign board and thereafter transmission of identified ...

With the rapid development of the Internet, webpages containing abused information such as pornography and gambling have emerged in an endless stream. These webpages are using various methods to evade traditional detection methods and which seriously ...

Charts or scientific plots are widely used visualizations for efficient knowledge dissemination from datasets. However, these charts are predominantly available in image format. There are various scenarios where these images are interpreted in the ...

Text recognition is one of the most important and challenging tasks in image-based sequence recognition, which has various potential applications in real life. In this paper, we propose a novel convolutional-recurrent neural network (CRNN) for text ...

Edge detection is a fundamental problem in image processing and computer vision, it plays an important role in the field of computer vision. Edge detection highlights significant changes in the image by identifying points in the digital image where the ...

This paper is based on CRNN model to recognize the text in the images of football matches scene, and two improvements are proposed. Considering the edge feature of text is strong, this paper adds MFM layers into CRNN model aiming to enhance the ...

Offline handwritten Hindi text recognition is a very tedious task. In this paper, a novel method using middle region of the words for recognition of handwritten Hindi text is proposed. A segmentation based approach is used for recognition. Although many ...