scholarly journals A Generic System for Processing Insurance

2021 ◽  
Author(s):  
Samundeswari S ◽  
Jeshoorin G ◽  
Vasanth M
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Health Check ◽  
Insurance Companies ◽  
Visualization Tool ◽  
Approval Process ◽  
Standard Set ◽  
Printed Text ◽  
Scanned Documents ◽  
Generic System

Insurance companies are regularly provided with health check reports by the buyers of insurance. Different forms of printed lab reports/health check reports have to be digitized for each value of captured parameters. Optical Character Recognition (OCR), is used to convert the images of handwritten, typed, printed text or any kind of scanned documents into machine-encoded text in order to digitize the values from the report. Conversion to this standard set of digital values will benefit in automating a lot of backend approval process. we collect the reports from the user and read the values from the report and scrutinize the values. Based on the values with the company’s standard set, the scrutinization is done and it is then visualized using any visualization tool. The result is presented to the user so that the user can get an idea whether he/she is eligible for insurance claim. The foremost objective of this paper is making the insurance backend approval process a lot easier and a quick response to the buyers.

scholarly journals Automated hierarchical classification of scanned documents using convolutional neural network and regular expression

2022 ◽  
Vol 12 (1) ◽  
pp. 1018
Author(s):  
Rifiana Arief ◽  
Achmad Benny Mutiara ◽  
Tubagus Maulana Kusuma ◽  
Hustinawaty Hustinawaty
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Regular Expression ◽  
Hierarchical Classification ◽  
Document Vector ◽  
Classification Prediction ◽  
Scanned Documents

<p>This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from pusat data teknologi dan informasi (technology and information data center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.</p>

scholarly journals Algorithm for auto annotation of scanned documents based on subregion tiling and shallow networks

2021 ◽  
Author(s):  
Komuravelli Prashanth ◽  
Kalidas Yeturu
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
High Performance ◽  
Performance Metrics ◽  
Training Data ◽  
Mean Average Precision ◽  
Average Precision ◽  
Squared Error Loss Function ◽  
Scanned Documents

<div>There are millions of scanned documents worldwide in around 4 thousand languages. Searching for information in a scanned document requires a text layer to be available and indexed. Preparation of a text layer requires recognition of character and sub-region patterns and associating with a human interpretation. Developing an optical character recognition (OCR) system for each and every language is a very difficult task if not impossible. There is a strong need for systems that add on top of the existing OCR technologies by learning from them and unifying disparate multitude of many a system. In this regard, we propose an algorithm that leverages the fact that we are dealing with scanned documents of handwritten text regions from across diverse domains and language settings. We observe that the text regions have consistent bounding box sizes and any large font or tiny font scenarios can be handled in preprocessing or postprocessing phases. The image subregions are smaller in size in scanned text documents compared to subregions formed by common objects in general purpose images. We propose and validate the hypothesis that a much simpler convolution neural network (CNN) having very few layers and less number of filters can be used for detecting individual subregion classes. For detection of several hundreds of classes, multiple such simpler models can be pooled to operate simultaneously on a document. The advantage of going by pools of subregion specific models is the ability to deal with incremental addition of hundreds of newer classes over time, without disturbing the previous models in the continual learning scenario. Such an approach has distinctive advantage over using a single monolithic model where subregions classes share and interfere via a bulky common neural network. We report here an efficient algorithm for building a subregion specific lightweight CNN models. The training data for the CNN proposed, requires engineering synthetic data points that consider both pattern of interest and non-patterns as well. We propose and validate the hypothesis that an image canvas in which optimal amount of pattern and non-pattern can be formulated using a means squared error loss function to influence filter for training from the data. The CNN hence trained has the capability to identify the character-object in presence of several other objects on a generalized test image of a scanned document. In this setting some of the key observations are in a CNN, learning a filter depends not only on the abundance of patterns of interest but also on the presence of a non-pattern context. Our experiments have led to some of the key observations - (i) a pattern cannot be over-expressed in isolation, (ii) a pattern cannot be under-xpressed as well, (iii) a non-pattern can be of salt and pepper type noise and finally (iv) it is sufficient to provide a non-pattern context to a modest representation of a pattern to result in strong individual sub-region class models. We have carried out studies and reported \textit{mean average precision} scores on various data sets including (1) MNIST digits(95.77), (2) E-MNIST capital alphabet(81.26), (3) EMNIST small alphabet(73.32) (4) Kannada digits(95.77), (5) Kannada letters(90.34), (6) Devanagari letters(100) (7) Telugu words(93.20) (8) Devanagari words(93.20) and also on medical prescriptions and observed high-performance metrics of mean average precision over 90%. The algorithm serves as a kernel in the automatic annotation of digital documents in diverse scenarios such as annotation of ancient manuscripts and hand-written health records.</div>

scholarly journals Comparison of text-image fusion models for high school diploma certificate classification

2020 ◽  
Vol 5 (1) ◽  
pp. 5-9
Author(s):  
Chandra Ramadhan Atmaja Perdana ◽  
Hanung Adi Nugroho ◽  
Igi Ardiyanto
Keyword(s):  
High School ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
High School Diploma ◽  
Fusion Model ◽  
Text And Image ◽  
Digital Era ◽  
Adaptive Fusion ◽  
Scanned Documents ◽  
Better Than

File scanned documents are commonly used in this digital era. Text and image extraction of scanned documents play an important role in acquiring information. A document may contain both texts and images. A combination of text-image classification has been previously investigated. The dataset used for those research works the text were digitally provided. In this research, we used a dataset of high school diploma certificate, which the text must be acquired using optical character recognition (OCR) method. There were two categories for this high school diploma certificate, each category has three classes. We used convolutional neural network for both text and image classifications. We then combined those two models by using adaptive fusion model and weight fusion model to find the best fusion model. We come into conclusion that the performance of weight fusion model which is 0.927 is better than that of adaptive fusion model with 0.892.

Behaviour-Based Clustering of Neural Networks

2011 ◽  
pp. 231-235
Author(s):  
María José Castro-Bleda ◽  
Slavador España-Boquera ◽  
Francisco Zamora-Martínez
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Clustering Algorithm ◽  
Training Data ◽  
Document Images ◽  
Supervised Classifiers ◽  
Overall Performance ◽  
Printed Text

The field of off-line optical character recognition (OCR) has been a topic of intensive research for many years (Bozinovic, 1989; Bunke, 2003; Plamondon, 2000; Toselli, 2004). One of the first steps in the classical architecture of a text recognizer is preprocessing, where noise reduction and normalization take place. Many systems do not require a binarization step, so the images are maintained in gray-level quality. Document enhancement not only influences the overall performance of OCR systems, but it can also significantly improve document readability for human readers. In many cases, the noise of document images is heterogeneous, and a technique fitted for one type of noise may not be valid for the overall set of documents. One possible solution to this problem is to use several filters or techniques and to provide a classifier to select the appropriate one. Neural networks have been used for document enhancement (see (Egmont-Petersen, 2002) for a review of image processing with neural networks). One advantage of neural network filters for image enhancement and denoising is that a different neural filter can be automatically trained for each type of noise. This work proposes the clustering of neural network filters to avoid having to label training data and to reduce the number of filters needed by the enhancement system. An agglomerative hierarchical clustering algorithm of supervised classifiers is proposed to do this. The technique has been applied to filter out the background noise from an office (coffee stains and footprints on documents, folded sheets with degraded printed text, etc.).

Segmentation of Arabic Characters

2013 ◽  
pp. 251-288
Author(s):  
Ahmed M. Zeki ◽  
Mohamad S. Zakaria ◽  
Choong-Yeun Liong
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Ground Truth ◽  
Main Challenge ◽  
Arabic Characters ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Segmentation Approach ◽  
Standard Set

The cursive nature of Arabic writing is the main challenge to Arabic Optical Character Recognition developer. Methods to segment Arabic words into characters have been proposed. This paper provides a comprehensive review of the methods proposed by researchers to segment Arabic characters. The segmentation methods are categorized into nine different methods based on techniques used. The advantages and drawbacks of each are presented and discussed. Most researchers did not report the segmentation accuracy in their research; instead, they reported the overall recognition rate which did not reflect the influence of each sub-stage on the final recognition rate. The size of the training/testing data was not large enough to be generalized. The field of Arabic Character Recognition needs a standard set of test documents in both image and character formats, together with the ground truth and a set of performance evaluation tools, which would enable comparing the performance of different algorithms. As each method has its strengths, a hybrid segmentation approach is a promising method. The paper concludes that there is still no perfect segmentation method for ACR and much opportunity for research in this area.

scholarly journals Improve OCR Accuracy with Advanced Image Preprocessing using Machine Learning with Python

2020 ◽  
Vol 9 (7) ◽  
pp. 1026-1030
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Image Preprocessing ◽  
Optical Character ◽  
Handwritten Text ◽  
Printed Text ◽  
Learning Machine

Optical Character Recognition or Optical Character Reader (OCR) is a pattern-based method consciousness that transforms the concept of electronic conversion of images of handwritten text or printed text in a text compiled. Equipment or tools used for that purpose are cameras and apartment scanners. Handwritten text is scanned using a scanner. The image of the scrutinized document is processed using the program. Identification of manuscripts is difficult compared to other western language texts. In our proposed work we will accept the challenge of identifying letters and letters and working to achieve the same. Image Preprocessing techniques can effectively improve the accuracy of an OCR engine. The goal is to design and implement a machine with a learning machine and Python that is best to work with more accurate than OCR's pre-built machines with unique technologies such as MatLab, Artificial Intelligence, Neural networks, etc.

scholarly journals Photometric Ligature Extraction Technique for Urdu Optical Character Recognition

2021 ◽  
Vol 11 (6) ◽  
pp. 7968-7973
Author(s):  
M. Kazmi ◽  
F. Yasir ◽  
S. Habib ◽  
M. S. Hayat ◽  
S. A. Qazi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Holistic Approach ◽  
Extraction Technique ◽  
Connected Component ◽  
Font Size ◽  
Optical Character ◽  
Segmentation Algorithms ◽  
Printed Text ◽  
Text Images

Urdu Optical Character Recognition (OCR) based on character level recognition (analytical approach) is less popular as compared to ligature level recognition (holistic approach) due to its added complexity, characters and strokes overlapping. This paper presents a holistic approach Urdu ligature extraction technique. The proposed Photometric Ligature Extraction (PLE) technique is independent of font size and column layout and is capable to handle non-overlapping and all inter and intra overlapping ligatures. It uses a customized photometric filter along with the application of X-shearing and padding with connected component analysis, to extract complete ligatures instead of extracting primary and secondary ligatures separately. A total of ~ 2,67,800 ligatures were extracted from scanned Urdu Nastaliq printed text images with an accuracy of 99.4%. Thus, the proposed framework outperforms the existing Urdu Nastaliq text extraction and segmentation algorithms. The proposed PLE framework can also be applied to other languages using the Nastaliq script style, languages such as Arabic, Persian, Pashto, and Sindhi.

Assistive Technology for People with Visual Impairments

1995 ◽  
Vol 26 (4) ◽  
pp. 8-10
Author(s):  
Danielle W. D. O'Korn ◽  
Joe E. Wheaton
Keyword(s):  
Assistive Technology ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Speech Synthesis ◽  
Visual Impairments ◽  
Individuals With Disabilities ◽  
Individuals With Disabilities Act ◽  
Optical Character ◽  
Printed Text

The Technology-Related Assistance Act for Individuals with Disabilities Act of 1988 states that for some individuals with disabilities, assistive technology is a necessity that enables them to engage in or perform many tasks. An important contribution computers have made to enhancing the lives of individuals who have visual impairments is that they have made printed text accessible to these individuals. Specific computer-related technologies for persons with visual impairments and blindness are presented, including speech synthesis, Braille output devices, optical character recognition, and magnification.

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