Layout Analysis for Scanned PDF and Transformation to the Structured PDF Suitable for Vocalization and Navigation

Information can include text, pictures and signatures that can be scanned into a document format, such as the Portable Document Format (PDF), and easily emailed to recipients around the world. Upon the document’s arrival, the receiver can open and view it using a vast array of different PDF viewing applications such as Adobe Reader and Apple Preview. Hence, today the use of the PDF has become pervasive. Since the scanned PDF is an image format, it is inaccessible to assistive technologies such as a screen reader. Therefore, the retrieval of the information needs Optical Character Recognition (OCR). The OCR software scans the scanned PDF file and through text extraction generates an editable text formatted document. This text document can then be edited, formatted, searched and indexed as well as translated or converted to speech. A problem that the OCR software does not solve is the accurate regeneration of the full text layout. This paper presents a technology that addresses this issue by closely preserving the original textual layout of the scanned PDF using the open source document analysis and OCR system (OCRopus) based on geometric layout and positioning information. The main issues considered in this research are the preservation of the correct reading order, and the representation of common logical structured elements such as section headings, line breaks, paragraphs, captions, and sidebars, foot-bars, running headers, embedded images, graphics, tables and mathematical expressions

State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines

In this paper we evaluate Optical Character Recognition (OCR) of 19th century Fraktur scripts without book-specific training using mixed models, i.e. models trained to recognize a variety of fonts and typesets from previously unseen sources. We describe the training process leading to strong mixed OCR models and compare them to freely available models of the popular open source engines OCRopus and Tesseract as well as the commercial state of the art system ABBYY. For evaluation, we use a varied collection of unseen data from books, journals, and a dictionary from the 19th century. The experiments show that training mixed models with real data is superior to training with synthetic data and that the novel OCR engine Calamari outperforms the other engines considerably, on average reducing ABBYYs character error rate (CER) by over 70%, resulting in an average CER below 1%.Comment: Submitted to DHd 2019 (https://dhd2019.org/) which demands a... creative... submission format. Consequently, some captions might look weird and some links aren't clickable. Extended version with more technical details and some fixes to follo

Optical Character Recognition

Our project aimed to understand, utilize and improve the open source Optical Character Recognizer (OCR) software, OCRopus, to better handle some of the more complex recognition issues such as unique language alphabets and special characters such as mathematical symbols. We extended the functionality of OCRopus to work with any language by creating support for UTF-8 character encoding. We also created a character and language model for the Hungarian language. This will allow other users of the software to preform character recognition on Hungarian input without having to train a completely new character model

Vigle: A Visual Graphical Learning Module on Optical Character Recognition

Students of the Arts and Humanities use OCR to convert scanned images of old text (pre-1800 AD). They need to know how digital text is extracted from the scanned image. Thanks to cell phones and images captured with them, understanding this is useful for everybody. The processing steps employed by a typical OCR software are, in order, Binarization, Deskew, Segmentation, Character Segmentation and Character Recognition. In this research project, a standalone asynchronous visual graphical learning environment (VIGLE) on Optical Character Recognition (OCR) was developed. Constructivist learning strategy was employed. The learning module was integrated into a website that works on mobile. The project attempts to generalize the instruction so that it is useful for everybody. Latest web technology was used for the implementation to achieve one stop interface, browser compatibility, responsive window sizing and interactive visual content. Binarization, Deskew and Segmentation modules were implemented in the time available. The VIGLE consists of a graphical representation and a visual interface to the lessons. Results show that the participants found both the graphical representation and the visual interface helpful. They found the incomplete learning module on OCR at best moderately useful in helping them digitize text

Active OCR: Tightening the Loop in Human Computing for OCR Correction

We propose a proof-of-concept application that will experiment with the use of active learning and other iterative techniques for the correction of eighteenth-century texts provided by the HathiTrust Digital Library and the 2,231 ECCO text transcriptions released into the public domain by Gale and distributed by the Text Creation Partnership (TCP) and 18thConnect. In an application based on active learning or a similar approach, the user could identify dozens or hundreds of difficult characters that appear in the articles from that same time period, and the system would use this new knowledge to improve optical character recognition (OCR) across the entire corpus. A portion of our efforts will focus on the need to incentivize engagement in tasks of this type, whether they are traditionally crowdsourced or through a more active, iterative process like the one we propose. We intend to examine how explorations of a users' preferences can improve their engagement with corpora of materials