Open Source Optical Character Recognition Research Articles

A neural network-enhanced methodology for the rapid establishment of local DRLs in interventional radiology: EVAR as a case example

PurposeTo develop a neural network-enhanced workflow for the automatic and rapid establishment/update of local diagnostic reference levels (DRLs) in interventional radiology (IR) using endovascular aneurysm repair (EVAR) procedures as a case example. MethodsRadiation dose reports were collected retrospectively for 46 consecutive EVAR procedures. These reports served as demonstrative data for the development of the proposed methodology. An algorithm was developed to receive multiple dose reports, automatically extract the kerma area product (KAP), air kerma (Ka,r), number of exposure images, and fluoroscopy time (FT) from each report and calculate the first, second, third quartiles as well as the maximum and minimum values of the extracted parameters. To extract the values of interest from the dose reports, Tesseract, an open-source optical character recognition (OCR) engine was employed. Furthermore, the accuracy and time efficiency of the proposed methodology were assessed. Specifically, the values extracted from the algorithm were compared with the ground truth values and the algorithm’s processing time was compared with the respective time needed to manually extract and process the values of interest. ResultsThe OCR-based algorithm managed to correctly recognize 182 from the 184 target values, resulting in an accuracy of 99%. Moreover, the proposed pipeline reduced the processing time for the establishment of DRLs by 98%. DRL value for EVAR procedures, set as the third quartile of KAP was found to be 551 Gy*cm2. ConclusionAn accurate and time-efficient workflow was developed for the establishment of local DRLs in interventional radiology.

Handwritten Text Recognition from Image

Abstract: A computer vision program called Handwritten Text Recognition (HTR) attempts to recognize and translate handwritten text from scanned or photographed images. In this project, we suggest implementing an HTR system using Tesseract and OpenCV. English, Chinese, and Arabic are all supported by the popular open-source optical character recognition (OCR) engine known as Tesseract. It is employed to find and identify printed text within photographs. On the other hand, OpenCV is a well-liked computer vision library that offers several tools for processing and analyzing images. The pre-processing step of the proposed system uses OpenCV to increase the input image's quality and OCR accuracy. After that, Tesseract receives the pre-processed image for text recognition. The extracted text is then saved in a text file after being identified. To enhance the quality of the input image, the project will use several pre-processing techniques, including deskewing, noise removal, and binarization. With the help of a sizable dataset of handwritten photographs, the Tesseract OCR engine is taught to recognize handwritten text more accurately. The HTR system can be used in a variety of fields, including document analysis, historical manuscript digitalization, and postal automation. It can also be applied in academic settings to help students translate their notes and assignments. Therefore, it is anticipated that the proposed HTR system employing Tesseract and OpenCV will offer a reliable and effective method for identifying and transcribing handwritten text from photographs.

Access to information research in the digital era

Access to information research in the digital era

An Experimental Performance Analysis on Robotics Process Automation (RPA) With Open Source OCR Engines: Microsoft Ocr And Google Tesseract OCR

Robotic Process Automation (RPA) plays an important role in the solution for automating human digital interaction in various software domains like IT, Business Strategy and so on. Optical Character Recognition(OCR) superimposes subtitled characters on an image.Here we use two Open source OCR engines, Google Tesseract OCR - It literally makes use of the open source Tesseract OCR Engine[1]. Microsoft OCR - This is another open source OCR engine accessible in the Robotics Process Automation tool, UiPath[1]. The technique of optical character recognition (OCR) has been used to transform printed text into editable text. In various applications, OCR is a very useful and common technique. OCR precision can be dependent on algorithms for text preprocessing and segmentation. Often because of different scale, design, orientation, complex picture context, etc., it is difficult to retrieve text from the image. We begin this paper with an introduction to the Optical Character Recognition (OCR) process, in this paper, we propose a research analysis to estimate the accuracy of two open source OCR engines with Robotics Process Automation(RPA). These RPA processes are created using the UiPath tool and our results compare the OCR engines performance during the execution with the workflow and save the results to our local storage or on cloud storage if cloud support enabled and here we used local storage for purpose, and the result analysis and error analysis was evaluated by manual process which also included excel formulas to calculate the accuracy of the extracted string with the original string.

Performance Analysis of Open Source Optical Character Recognition

Optical character recognition is the process of conversion of images of text into machine-encoded text electronically or mechanically. The text on image can be handwritten, typed or printed. Some of the examples of image source can be a picture of a document, a scanned document or a text which is superimposed on an image. Most optical character recognition system does not give a 100% accurate result. This project aims at analyzing the error rate of a few open source optical character recognition systems (Boxoft OCR, ABBY, Tesseract, Free Online OCR etc.) on a set of diverse documents and makes a comparative study of the same. By this, we can study which OCR is the best suited for a document.

Polarity Identification for Handwritten Text in Multilingual Documents Using Open Source Optical Character Recognition Tools

Sentimental analysis is a prerequisite for many applications. We propose a model which scans handwritten text in English and Kannada languages by a CamScanner and then translated into editable text by using various Open Source Optical Character Recognition tools. The performances of different OCRs are analyzed and tabulated. Sentimental analysis is performed on the statements written in both English and Kannada languages using Wordnet, Algorithmia Rest API and local dictionaries and we have obtained the satisfied results. The same sentimental analysis module is also applied on customer reviews for the mobile product and reviews are taken from Amazon Web Services. The opinion of the customer about the product can be identified correctly.

Balinese character recognition on mobile application based on tesseract open source OCR engine

Balinese script is a part of Balinese culture is rarely used today. The Provincial Government of Bali with the Governor Regulation number 80 of 2018 is trying to preserve the Balinese language and script. This study aimed at preserving the Balinese script through a mobile technology approach which is the recent trend with worldwide coverage for supporting ubiquitous learning. This research integrated the Android application to recognize Balinese characters in the form of images into text with Tesseract open source Optical Character Recognition (OCR) engine. The input of this application is a Balinese script image captured by a mobile camera or from a Balinese script image. The application recognized input image into text that can be further processed based on training data available in the application. The new Balinese script training data was created based on eighteen Balinese script’s basic syllables and numbers only. This application can be operated offline with mobile hardware that supports camera functions. The result for testing for 50-word, recognition was 62% obtained in good quality image-based Bali-Simbar font. This application can be further developed to recognize other character repertoire i.e., vowels (Akśara Suara), semi vowels (Arda Suara), additional syllables (Akśara Şwalalita), and sound killers (Pangangge Tengenan).

Machine Replication of Human Perusing using Optical Character Recognition with Tesseract

Optical Character Recognition is the machine replication of human perusing. Electronic Conversion of examined pictures where picture can be type composed or printed content. It is executed utilizing Google's open source Optical Character Recognition programming called Tesseract. The OCR accepts picture as the information, gets content from that picture and afterward changes over it into whatever other language that the client needed. This framework can be helpful in different applications like banking, legitimate industry, explorers’ different ventures, and home and office robotization. It for the most part intended for individuals who are unfit to peruse any sort of content archives and to diminish the weight of information passage occupations.[4]

Recognition of Hindi and Bengali Handwritten and Typed Text from Images using Tesseract on Android Platform

The concept of digitization has marked a revolution in the area of data conversion, data storage and data sharing by converting non-editable typographic & handwritten text into editable electronic text. Though numerous such works have been carried out across the world in various languages using Optical Character Recognition (OCR), satisfactory output has been observed only in a few languages. This paper is an endeavor towards taking a step ahead in the digitization of two of the most extensively spoken languages in the Indian sub-continent – Hindi and Bengali - using Google’s open source OCR Engine, Tesseract. Working on the scripts of these two languages of Brahmi origin has its own challenges owing to their varied traits of character segmentation and word formation. Here, the training of Tesseract with data sets of Hindi and Bengali typographic and handwritten characters has been integrated with an inimitable pre-processing stage involving input image customization and image augmentation that significantly enhances the image quality allowing Tesseract to offer more accurate results, especially in cases of handwritten texts and obscure images. Besides, it also incorporates the features of English translation and text to speech translation which render their significance among the non-natives and visually impaired mass. The focal idea of this paper has been to reach out to an extended mass by enabling digitization on the Android platform. Comparative analysis carried out on three distinctive parameters - on images with typographic texts, handwritten texts and on inferior quality images - shows that the paper, to a certain extent, does succeed in projecting superior output in at least two cases as compared to the most consistent Android application of today’s time.

OCR4all—An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings

Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years, great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout analysis and segmentation, character recognition, and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper, we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. While a variety of materials can already be processed fully automatically, books with more complex layouts require manual intervention by the users. This is mostly due to the fact that the required ground truth for training stronger mixed models (for segmentation, as well as text recognition) is not available, yet, neither in the desired quantity nor quality. To deal with this issue in the short run, OCR4all offers a comfortable GUI that allows error corrections not only in the final output, but already in early stages to minimize error propagations. In the long run, this constant manual correction produces large quantities of valuable, high quality training material, which can be used to improve fully automatic approaches. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. During experiments, the fully automated application on 19th Century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. Furthermore, on very complex early printed books, even users with minimal or no experience were able to capture the text with manageable effort and great quality, achieving excellent Character Error Rates (CERs) below 0.5%. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.

An Accuracy Examination of OCR Tools

In this research paper, the authors have aimed to do a comparative study of optical character recognition using different open source OCR tools. Optical character recognition (OCR) method has been used in extracting the text from images. OCR has various applications which include extracting text from any document or image or involves just for reading and processing the text available in digital form. The accuracy of OCR can be dependent on text segmentation and pre-processing algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, a complex background of image etc. From vehicle number plate the authors tried to extract vehicle number by using various OCR tools like Tesseract, GOCR, Ocrad and Tensor flow. The authors in this research paper have tried to diagnose the best possible method for optical character recognition and have provided with a comparative analysis of their accuracy

Store Sign Text Recognition for Wearable Navigation Assistance System

With the development of computer vision, wearable computing technologies not only have changed our lifestyle, but also have provided much convenience for vulnerable road users, especially the Visually Impaired (VI) pedestrians. VI people have difficulties in locating and socializing due to the limitations of traditional assistive tools, e.g., the inability to recognize text. Text plays a significant role in various aspects, which can convey abundant semantic information of the scene. In recent years, text detection and recognition has made huge progress which makes it possible for VI people to understand the surroundings by using scene text information. In this paper, a text recognition system is proposed to help VI people to perceive store sign text. Firstly, we locate the text on the sign with the aim of leading VI pedestrian to reach the destination store. Towards this end, an objection detection network is integrated into the system to extract Regions of Interest (ROI) in complex real-world scenarios. In order to fulfil real-time assistance, an efficient detection network named Single Shot MultiBox Detector (SSD) has been made light-weight and embedded in the wearable system. Secondly, we leverage an open-source optical character recognition (OCR) instrument to recognize the detected text. Afterwards, we introduce the collected dataset and critical training tips for the task. Finally, a comprehensive set of experiments on our dataset demonstrates that our approach significantly improves the precision and make the recognition robust even in real-world settings. Based on our approach, the wearable system can feedback the recognized text in real time and assist the VI people during their every independent navigation.

Towards Syriac Digital Corpora: Evaluation of Tesseract 4.0 for Syriac OCR

Abstract This paper summarizes the results of an extensive test of Tesseract 4.0, an open-source Optical Character Recognition (OCR) engine with Syriac capabilities, and ascertains the current state of Syriac OCR technology. Three popular print types (S14, W64, and E22) representing the Syriac type styles Estrangela, Serto, and East Syriac were OCRed using Tesseract’s two different OCR modes (Syriac Language and Syriac Script). Handwritten manuscripts were also preliminarily tested for OCR. The tests confirm that Tesseract 4.0 may be relied upon for printed Estrangela texts but should be used with caution and human revision for Serto and East Syriac printed texts. Consonantal accuracy lies around 99% for Estrangela, between 89% and 94% for Serto, and around 89% for East Syriac. Scholars may use Tesseract to OCR Estrangela texts with a high degree of confidence, but further training of the engine will be required before Serto and East Syriac texts can be smoothly OCRed. In all type styles, human revision of the OCRed text is recommended when scholars desire an exact, error-free corpus.

Prototype to Help Visually Impaired Person in Reading Printed Learning Materials using Raspberry PI

This paper presents a prototype to help visually impaired persons in reading printed learning materials using Raspberry PI. Tesseract an open source optical character recognition technique is used extract texts in printed images and converted to audio output using text-to-speech conversion software. Prototype is experimented using printed text pages with various font sizes and line spacing as test cases. Results show that the prototype is better in converting printed texts to speech. However quality of image, font size, and line space affects performance of prototype in converting printed texts to speech..

Enhanced portable text to speech converter for visually impaired

The handwritten text reader is designed to help the visually impaired listen to an audio read-back of printed and handwritten scanned text. A hand-held page scanner is used to scan the text to be read. The image from the scanner is sent to the application in the paired Android phone over Bluetooth. An open source optical character recognition (OCR) engine, Tesseract is used to extract the text from the image, and this extracted text is converted to speech. Tesseract OCR engine is further trained to recognise handwritten text for a specific user. This OCR engine is trained with handwritten datasets. In addition to English, the application supports two regional languages - Hindi and Bengali.

Multileaf-collimator daily quality assurance of Vero4DRT system: our one-year experience

Purpose: We assessed the daily quality assurance (QA) of multi-leaf collimator (MLC) using the Vero4DRT system. Methods: As part of daily MLC QA, the irradiation field was set to 100 × 150 mm 2 with a gantry angle of 0 o. Only the leaf positioning error values only were displayed. We developed an in-house program to easily acquire these values using an open source optical character recognition engine. This test was implemented between 24 August 2015 and 23 August 2016. Results: The maximum leaf positioning error was 0.40 mm in both banks. In addition, the maximum deviation was 0.10 mm in both banks. The average and standard deviation for left and right banks were 0.19 mm ± 0.11 mm and 0.15 mm ± 0.09 mm, respectively. In our one-year measurement, the leaf positioning error was less than 0.50 mm. Therefore, if the leaf position error for daily MLC QA exceeded 0.50 mm, then an external intervention is required. Conclusion: The daily MLC QA of our one-year evaluation of the Vero4DRT system demonstrates an excellent leaf accuracy and reproducibility, thereby giving confidence in the quality of the treatment.

Adoption of an Open Source Optical Character Recognition (OCR) for Database Buildup of the Students’ Scholastic Records

Adoption of an Open Source Optical Character Recognition (OCR) for Database Buildup of the Students’ Scholastic Records

Energy‐efficient adaptive optical character recognition for wearable devices

As the computing power of mobile/wearable devices increases, computation-intensive real-time optical character recognition (OCR) becomes feasible for high-resolution images. Developing mobile/wearable OCR applications is challenging because they should perform highly accurate OCR within users' tolerable waiting time and achieve low energy consumption. An adaptive power management scheme is presented that predicts the execution time for OCR and minimises its energy consumption via dynamic voltage and frequency scaling while meeting its time constraint. Tesseract, a popular open source OCR engine, is used to verify the proposed scheme. The experimental results show up to 48.25% reduction in energy consumption (with an average reduction of 34.53%).

Can Morphological Analyzers Improve the Quality of Optical Character Recognition?

Optical Character Recognition (OCR) can substantially improve the usability of digitized documents. Language modeling using word lists is known to improve OCR quality for English. For morphologically rich languages, however, even large word lists do not reach high coverage on unseen text. Morphological analyzers offer a more sophisticated approach, which is useful in many language processing applications. is paper investigates language modeling in the open-source OCR engine Tesseract using morphological analyzers. We present experiments on two Uralic languages Finnish and Erzya. According to our experiments, word lists may still be superior to morphological analyzers in OCR even for languages with rich morphology. Our error analysis indicates that morphological analyzers can cause a large amount of real word OCR errors.

Simple and Efficient Method for Region of Interest Value Extraction from Picture Archiving and Communication System Viewer with Optical Character Recognition Software and Macro Program

The objectives are: 1) to introduce a simple and efficient method for extracting region of interest (ROI) values from a Picture Archiving and Communication System (PACS) viewer using optical character recognition (OCR) software and a macro program, and 2) to evaluate the accuracy of this method with a PACS workstation. This module was designed to extract the ROI values on the images of the PACS, and created as a development tool by using open-source OCR software and an open-source macro program. The principal processes are as follows: (1) capture a region of the ROI values as a graphic file for OCR, (2) recognize the text from the captured image by OCR software, (3) perform error-correction, (4) extract the values including area, average, standard deviation, max, and min values from the text, (5) reformat the values into temporary strings with tabs, and (6) paste the temporary strings into the spreadsheet. This principal process was repeated for the number of ROIs. The accuracy of this module was evaluated on 1040 recognitions from 280 randomly selected ROIs of the magnetic resonance images. The input times of ROIs were compared between conventional manual method and this extraction module-assisted input method. The module for extracting ROI values operated successfully using the OCR and macro programs. The values of the area, average, standard deviation, maximum, and minimum could be recognized and error-corrected with AutoHotkey-coded module. The average inputtimes using the conventional method and the proposed module-assisted method were 34.97 seconds and 7.87 seconds, respectively. A simple and efficient method for ROI value extraction was developed with open-source OCR and a macro program. Accurate inputs of various numbers from ROIs can be extracted with this module. The proposed module could be applied to the next generation of PACS or existing PACS that have not yet been upgraded.