Flexible Augmented Reality-Based Health Solution for Medication Weight Establishment

An internet of things (IoT) is an exclusive method, were its impact on the enactments of human life is very trendy. This research on library control system operates on the basis of IoT and optical character recognition (OCR) algorithm rules and its training. A closed-circuit television (CCTV) watched mechanism is created to control the book issuing and returning phenomenon via tag studying system in the library. In this proposed work text file is converted into an audio file. This audio file is being played and the contents of the book can be heard via the headset. This unique function of the OCR helps blind people. Now a days OCR widely focused in machine processes such as machine transformation, text to speech extraction and text data mining. It utilized in various area of research in artificial intelligence, computer vision and pattern recognition. Using OCR to scan the damaged book in the library converted into pdf format the book gets new life and sharing the contents to multiple readers. In this paper aims to implement IoT based library management system to maintaining books in digital format.

Lack of handwriting automatization in childhood can cause difficulties within and outside the school context. Therefore, the objective quantification of the handwriting performance is key. A Smart Ink Pen (SIP) used on paper demonstrated its validity in characterizing primary school children's handwriting process, although missing information on the handwriting product. To overcome this limitation, a trace reconstruction algorithm was developed, based on the force and IMU signals measured by the SIP. A total of 353 words "uno", written in cursive from the BVSCO-3 battery by 50 Italian students of the 5th grade of primary school, was reconstructed. The quality of the reconstructions was validated through an Optical Character Recognition (OCR) algorithm (Google Vision), using the scans of the actual traces as a reference. The character recognition rates were 81.02% and 59.49%, the character error rates 21.48% and 47.65%, for scans and reconstructions, respectively. A deeper analysis revealed that 15% of the reconstructions were read in the opposite direction by the OCR algorithm, likely due to a non sufficient sampling rate for the last portion of the words. A characterization of the differences between the good, bad and opposite reconstructions allowed to identify some directions of improvement. An increase of the SIP sampling rate, a better modeling of the thickness of the trace, a finer estimation of the relative distance between the IMU sensor and the tip and the reconstruction of the tip trajectory during in-air movements could improve the trace reconstruction algorithm. In addition, the possibility to leverage transfer learning approaches on the OCR algorithm using the reconstructed traces for additional training could further improve the performances in terms of character recognition rate. However, the preliminary results obtained are promising and highlight the possibility of reconstructing handwriting traces while maintaining the naturalness of the gesture.Clinical relevance- This contribution establishes the possibility of reconstructing handwritten traces from the kinematic and force signals recorded by an ecological sensorized ink pen.

Since the COVID-19 pandemic, numerous jobs have become necessary, including the storing and sharing of printed material across computers. One simple way to save data from printed papers to a computer system is to scan them first and then save them as images. However, it would be quite challenging to extract or query text or other information from these photo files to reuse this information. As a result, a method for automatically retrieving and storing information, particularly text, from picture files is required. Optical character recognition (OCR) is an ongoing research topic that aims to create a computer system capable of extracting and processing text from images. To accomplish successful automation, certain significant problems must be identified and addressed. The font properties of characters in paper documents, as well as image quality, are only a few of the latest problems. Characters may not be recognized correctly by the computer system because of many complexities. So, in this study, authors look into OCR in four different contexts and apply them to get our results. However, every OCR is further followed by these two steps. First, a comprehensive explanation of the challenges that may develop during the OCR phases is provided. The key phases of an OCR system are then executed, including pre-processing, segmentation, normalization, feature extraction, classification, and post-processing. It can be used with deep learning software to provide OCR data which is very useful for robotic and AI applications.

PurposeDigitization of historical documents is a challenging task in many digital humanities projects. A popular approach for digitization is to scan the documents into images, and then convert images into text using optical character recognition (OCR) algorithms. However, the outcome of OCR processing of historical documents is usually inaccurate and requires post-processing error correction. The purpose of this paper is to investigate how crowdsourcing can be utilized to correct OCR errors in historical text collections, and which crowdsourcing methodology is the most effective in different scenarios and for various research objectives.Design/methodology/approachA series of experiments with different micro-task’s structures and text lengths were conducted with 753 workers on the Amazon’s Mechanical Turk platform. The workers had to fix OCR errors in a selected historical text. To analyze the results, new accuracy and efficiency measures were devised.FindingsThe analysis suggests that in terms of accuracy, the optimal text length is medium (paragraph-size) and the optimal structure of the experiment is two phase with a scanned image. In terms of efficiency, the best results were obtained when using longer text in the single-stage structure with no image.Practical implicationsThe study provides practical recommendations to researchers on how to build the optimal crowdsourcing task for OCR post-correction. The developed methodology can also be utilized to create golden standard historical texts for automatic OCR post-correction.Originality/valueThis is the first attempt to systematically investigate the influence of various factors on crowdsourcing-based OCR post-correction and propose an optimal strategy for this process.

Character recognition has been very popular and interested area for researches, and it continues to be a challenging and impressive research topic due to its diverse applicable environment. The optical character recognition has been introduced as a fast and accurate method to convert both existing text images as well as large archives of existing paper documents to editable digital text format. However, existing optical character recognition algorithms suffer from flawed tradeoffs between accuracy and speed, making them less effective and impractical for large and complex documents. This paper describes a suggested method for Assyrian optical character recognition using modified back propagation artificial neural network based on moments. The experimental results show that the proposed method achieves higher recognition accuracy rate in compared with the standard algorithm.

Many documents must be processed and scanned digitally, especially during this pandemic, physical documents often have to be reprocessed using word processing applications. This process takes a lot of time especially if the document is damaged, lost and does not have a copy. This can be overcome by using the Optical Character Recognition (OCR) algorithm. OCR converts letter images into computer-recognized ASCII characters. Image files are relatively larger than their ASCII text files. From the existing problems, the researchers tried to find a solution by creating an OCR application program to simplify the process. The created OCR program is successfully used to scan or scan a physical document if the document is lost or damaged, and can save time on document creation, processing and typing. Keywords: Algorithm, Optical Character Recognition, ASCII

Novel video stabilization for real-time optical character recognition applications

Thinning is usually regarded as a process of deleting boundary pixels of a character pattern until all strokes are of one pixel in width without deforming the original stroke configuration and connection. Suppose an OCR system uses deformed skeletons as recognition features, we may see that a `T' may erroneously be recognized as a `Y' or `r.' In order to preserve original stroke features, we propose that global attributes should be considered in the thinning procedure. In this paper, we present a new 3 X 3 window-based binary thinning method that considers both local and global attributes in each thinning iteration. We have designed and implemented a fast thinning algorithm to incorporate these two attributes. This algorithm can (1) prevent any excessive removing of pixels at the junction of two strokes or at the end of a stroke, which causes Y-shaped or shortened skeletons, and (2) can detect and remove any spooky type noise (one or two pixels standing on the surface of a stroke) which usually produces spiky skeletons in most of the previously proposed thinning algorithms. Experiment results show that our thinning method can preserve precise skeleton features of the original character patterns.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

It is quite difficult for visually impaired individuals to read a physically printed material. It is a necessity to provide information flow and communication between visually impaired individuals and other individuals. For this purpose, an alphabet has been developed for visually impaired individuals. With this alphabet, which is called the Braille alphabet, it is possible for visually impaired individuals to access written culture. There are special letters in the alphabet designed for the visually impaired. With the development of technology, studies in the field of Braille alphabet have increased. Optical character recognition technology can convert the letters on files such as pdf, pictures, photos into understandable data by selecting and separating them. Speech recognition algorithm, computer, tablet, mobile phone, voice recorder devices, etc. It is the process of detecting and recognizing the human voice through a microphone with technological products. Within the scope of the study, a device has been developed for Braille alphabet by using optical character recognition technology and speech recognition algorithm. First, the images of each page of the books that were requested to be translated into Braille alphabet were taken. Then, a transformation was performed using a specific character set with the optical character recognition algorithm. Audiobooks, on the other hand, were first converted into text and then into Braille alphabet using speech recognition algorithm. With these converted characters, a physical hardware device has been developed to display Braille letters that visually impaired individuals can read faster.

With thousands of books across hundreds of disciplines present in a library, manual allocation of books is both time consuming, human intensive, as well as inefficient in terms of monetary costs. The presence of manual labour while providing employment to a section of society tends to slow down the process of lending in addition to increasing the probability of errors. The proposed research work attempts at automating the library book management system there by reducing latency and long queues as well as the potential of making mistakes in the distribution of books. Recent advancements of deep neural networks have improved textual recognition in natural scenes. The system proposed employs a neural network model for text detection and recognition, having the potential to reduce and even eliminate manual labour performed by the staff. This paper attempts to investigate neural networks for the application of library management by proposing our own Optical Character Recognition (OCR) and text matching algorithms. This task further introduces challenges such as getting the exact name of the book as well as dealing with distorted images and varied backgrounds. The system thus developed performs very well with book covers producing results over 60% in accuracy against current models that provide about 40% thereby making the system achieve state of the art performance on book cover information retrieval.

Advances in the digital world have attracted the attention of many researchers in terms of developing digital forensics. Using machine learning methods for print source identification is one of the developing areas in the field of digital forensics. In this paper, a new method is presented for printer source identification by modeling the primary Local Binary Pattern (LBP) features in the total variable printer space and extracting the secondary features based on the joint factor analysis. Only one low-dimensional i-vector feature is employed for each document image without using any optical character recognition (OCR) algorithm or similar processes. This property eliminates the requirement for the majority voting algorithm and reduces the computational cost of the classification process. Furthermore, the proposed algorithm is not limited to a specific language or character set. The capabilities of the proposed method in extracting useful discriminant information from the sparse print shadow texture are revealed through simulation. The simulation results showed that the proposed algorithm obtained the accuracy of 98.48% by refining the basic features of LBP, which is comparable to the results of the state-of-the-art approaches in this field.

A simulation is described which provides a vehicle for the synthesis and subsequent performance analysis of optical character recognition (OCR) algorithms aimed at solving some of the most difficult problems encountered in OCR.

This paper introduces a multimodal framework for clustering spam images received in unsolicited emails. Spam images in the same cluster have similar visual and textual contents and could be generated by a common spam source. To perform the clustering task, we first extract three main categories of features: 1) Visual features, extracted by pretrained convolutional neural networks (CNNs); 2) Layout features, the location of illustrations in the spam images; 3) Text features extracted by optical character recognition (OCR) algorithm. We then use a two-stage hierarchical clustering framework to form clusters based on the pair-wise similarity matrices of the extracted features. We evaluate the performance of the proposed approach on a 2,100 spam image dataset collected from three months of emails. The experimental results show that the proposed method achieved satisfactory clustering outcomes in terms of an external entropy-based metric, the V-measure.

Spammers continues to uses new methods and the types of email content becomes more difficult, text-based antispam methods are not good enough to prevent spam. Spam image making techniques are designed to bypass well-known image spam detection algorithms like optical character recognition (OCR) algorithm. As the use different methods to create image spam are increased, there must be an algorithm to prevent this type of spams. So using the different properties of images attached in the mail like width and height indicated in header of image, the aspect ratio of width and height, file size, image area, compression, owner, colors and file format of image. We can develop an algorithm which is used to detect the spam based images using the properties of the attached images. Also it is maintains a database for email addresses of spammers and blockthe email address based on result of detection process. Keywords—Spam, Image Spam, Spammers.

Anonymization of DICOM electronic medical records for radiation therapy