Sophisticated and modernized library running system with OCR algorithm using IoT

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.

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.

The trend of personalized medicine and the increasing flexibility of drug dosage relevant goals of the 21st century represent the foundation for the current research. To obtain doses smaller than the smallest available, physicians frequently write prescriptions for children and adults, without preserving the integrity of the pill. Moreover, patients purchase large amounts of medication for cost-saving reasons. To support the correct administration of the remedies and the partial alignment to the personalized treatment trend, this paper proposes a flexible and user-friendly solution for determining the medication quantity given to patients, using augmented reality and optical character recognition algorithm capabilities. Via the MATLAB development environment and a Logitech HD Pro C920 webcam, the results were 80% correct in identifying the cutting position of the pill, by means of the Hough transform, and 30% correct in weight recognition exploitation using an optical character recognition (OCR) algorithm. In future work, a higher resolution camera and a more powerful computer can be used to increase the percentages mentioned above. In addition, a 3D scan of the pill fragmentation, combined with the weight recognition functionality, could increase the accuracy of the splitting procedure, conducted by the patient or the patient caretaker.

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.

evelopment of OCRs for Indian script is an active area of activity today. Optical character recognition (OCR) is the mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-editable text. In simple words OCR is a visual recognition process that turns printed or written text into an electronic character-based file. OCR is a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in the field continues, the focus on OCR has shifted to implementation of proven techniques. A lot of work had been carried out for OCR at international scenario but in Indian context a concrete approach for character recognition is still required as scripts of Indian languages are from the group of most complex scripts and it is very hard to recognize them. Indian scripts present great challenges to an OCR designer due to the large number of letters in the alphabet, the sophisticated ways in which they combine, and the complicated graphemes they result in. The problem is compounded by the unstructured manner in which popular fonts are designed. There is a lot of common structure in the different Indian scripts. All existing OCR systems developed for various Indian scripts do not provide sufficient efficiency due to various factors. The objective of this paper is to discuss a more efficient character recognition technique. This paper introduces a new technical approach to recognize Indian script characters which are unpredictable due to different problems in other OCR‟s.

The Optical Character Recognition (OCR) models have improved a lot in the last years thanks to the advance of machine learning techniques. However, there is still a great challenge when images are too distorted, such as old scanned documents, where the characters are blurred or/and with irregular backgrounds. Document digitization is a crucial task in many enterprises to get all their information available. For industries with long life cycles, such as energy and mining, old reports are relevant. This paper proposes a methodology to improve the performance of OCR algorithms. It uses conditional generative adversarial networks (GANs) for improving the quality of rendered text in images, which maximizes the performance of the OCR algorithms. We created a synthetic dataset, which emulates most of the problems presents on scanned documents, to train the network. Besides synthetic data, we tested our methodology on real images collected from USA journals and Brasilian thesis. The results showed relevant improvements, in particular when evaluated images were too distorted. Besides, we performed experiments with text in English and Portuguese, demonstrating the efficiency of our work in different languages. The proposed methodology overcomes most of the problems that impact the quality of Tesseract, the OCR algorithm used in this work. Our results demonstrated the capability of the proposed OCR system over other algorithms in low-quality documents. Metrics of errors showed a decrease of up to 50% regarding the best results without using our methodology.

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

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.

Novel video stabilization for real-time optical character recognition applications

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.