A tool for facilitating OCR postediting in historical documents

Optical character recognition (OCR) for historical documents is a complex procedure subject to a unique set of material issues, including inconsistencies in typefaces and low quality scanning. Consequently, even the most sophisticated OCR engines produce errors. This paper reports on a tool built for postediting the output of Tesseract, more specifically for correcting common errors in digitized historical documents. The proposed tool suggests alternatives for word forms not found in a specified vocabulary. The assumed error is replaced by a presumably correct alternative in the post-edition based on the scores of a Language Model (LM). The tool is tested on a chapter of the book An Essay Towards Regulating the Trade and Employing the Poor of this Kingdom. As demonstrated below, the tool is successful in correcting a number of common errors. If sometimes unreliable, it is also transparent and subject to human intervention

IMPROVING THE EFFICIENCY OF TESSERACT OCR ENGINE

This project investigates the principles of optical character recognition used in the Tesseract OCR engine and techniques to improve its efficiency and runtime. Optical character recognition (OCR) method has been used in converting printed text into editable text in various applications over a variety of devices such as Scanners, computers, tablets etc. But now Mobile is taking over the computer in all the domains but OCR still remains one not so conquered field. So programmers need to improve the efficiency of the OCR system to make it run properly on Mobile devices. This paper focuses on improving the Tesseract OCR efficiency for Hindi language to run on Mobile devices as there a not many applications for the same and most of them are either not open source or not for mobile devices. Improving Hindi text extraction will increase Tesseract\u27s performance for Mobile phone apps and in turn will draw developers to contribute towards Hindi OCR . This paper presents a preprocessing technique being applied to the Tesseract Engine to improve the recognition of the characters keeping the runtime low. Hence the system runs smoothly and efficiently on mobile devices(Android) as it does on the bigger machines

Knowledge Elicitation in Deep Learning Models

Embora o aprendizado profundo (mais conhecido como deep learning) tenha se tornado uma ferramenta popular na solução de problemas modernos em vários domínios, ele apresenta um desafio significativo - a interpretabilidade. Esta tese percorre um cenário de elicitação de conhecimento em modelos de deep learning, lançando luz sobre a visualização de características, mapas de saliência e técnicas de destilação. Estas técnicas foram aplicadas a duas arquiteturas: redes neurais convolucionais (CNNs) e um modelo de pacote (Google Vision). A nossa investigação forneceu informações valiosas sobre a sua eficácia na elicitação e interpretação do conhecimento codificado. Embora tenham demonstrado potencial, também foram observadas limitações, sugerindo espaço para mais desenvolvimento neste campo. Este trabalho não só realça a necessidade de modelos de deep learning mais transparentes e explicáveis, como também impulsiona o desenvolvimento de técnicas para extrair conhecimento. Trata-se de garantir uma implementação responsável e enfatizar a importância da transparência e compreensão no aprendizado de máquina. Além de avaliar os métodos existentes, esta tese explora também o potencial de combinar múltiplas técnicas para melhorar a interpretabilidade dos modelos de deep learning. Uma mistura de visualização de características, mapas de saliência e técnicas de destilação de modelos foi usada de uma maneira complementar para extrair e interpretar o conhecimento das arquiteturas escolhidas. Os resultados experimentais destacam a utilidade desta abordagem combinada, revelando uma compreensão mais abrangente dos processos de tomada de decisão dos modelos. Além disso, propomos um novo modelo para a elicitação sistemática de conhecimento em deep learning, que integra de forma coesa estes métodos. Este quadro demonstra o valor de uma abordagem holística para a interpretabilidade do modelo, em vez de se basear num único método. Por fim, discutimos as implicações éticas do nosso trabalho. À medida que os modelos de deep learning continuam a permear vários setores, desde a saúde até às finanças, garantir que as suas decisões são explicáveis e justificadas torna-se cada vez mais crucial. A nossa investigação sublinha esta importância, preparando o terreno para a criação de sistemas de inteligência artificial mais transparentes e responsáveis no futuro.Though a buzzword in modern problem-solving across various domains, deep learning presents a significant challenge - interpretability. This thesis journeys through a landscape of knowledge elicitation in deep learning models, shedding light on feature visualization, saliency maps, and model distillation techniques. These techniques were applied to two deep learning architectures: convolutional neural networks (CNNs) and a black box package model (Google Vision). Our investigation provided valuable insights into their effectiveness in eliciting and interpreting the encoded knowledge. While they demonstrated potential, limitations were also observed, suggesting room for further development in this field. This work does not just highlight the need for more transparent, more explainable deep learning models, it gives a gentle nudge to developing innovative techniques to extract knowledge. It is all about ensuring responsible deployment and emphasizing the importance of transparency and comprehension in machine learning. In addition to evaluating existing methods, this thesis also explores the potential for combining multiple techniques to enhance the interpretability of deep learning models. A blend of feature visualization, saliency maps, and model distillation techniques was used in a complementary manner to extract and interpret the knowledge from our chosen architectures. Experimental results highlight the utility of this combined approach, revealing a more comprehensive understanding of the models' decision-making processes. Furthermore, we propose a novel framework for systematic knowledge elicitation in deep learning, which cohesively integrates these methods. This framework showcases the value of a holistic approach toward model interpretability rather than relying on a single method. Lastly, we discuss the ethical implications of our work. As deep learning models continue to permeate various sectors, from healthcare to finance, ensuring their decisions are explainable and justified becomes increasingly crucial. Our research underscores this importance, laying the groundwork for creating more transparent, accountable AI systems in the future

Claims processing automation - Modernization of an insurance company internal process

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceDeep learning and text mining are involved in the research. This work includes the project I developed together with my colleagues at SAS Institute during my internship experience. In this project we had to support an Insurance company for the automation of their existing claim processing system. In fact, as of today, the procedure of reading the incoming claim requests, selecting the useful information and extracting it to a data management software, is done manually for hundreds of claims every day. The job required by the insurance company is to substitute the existing procedure with an automated one, by implementing an OCR system to read the raw data contained in the documents sent by the customers and transform it into clean and useful information to be inserted into the data management software. This research will show the investigation on how to deal with this problem and the objective is to automate the classification of the documents for the company, to provide them a system to prioritize the most urgent documents and to execute some technical and administrative checks on the extracted information. The automation is shown to be feasible; the completeness and accuracy of the information extracted are solid, proving that this specific task in the insurance company sector can be realized and help to reduce costs while improving time performance