Despite the significant success in the field of text recognition, complex and unsolved problems still exist in this field. In recent years, the recognition accuracy of the English language has greatly increased, while the problem of recognition of hieroglyphs has received much less attention. Hieroglyph recognition or image recognition with Korean, Japanese or Chinese characters have differences from the traditional text recognition task. This article discusses the main differences between hieroglyph languages and the Latin alphabet in the context of image recognition. A light-weight method for recognizing images of the hieroglyphs is proposed and tested on a public dataset of Korean hieroglyph images. Despite the existing solutions, the proposed method is suitable for mobile devices. Its recognition accuracy is better than the accuracy of the open-source OCR framework. The presented method of training embedded net bases on the similarities in the recognition data.

Recognition of images of Korean characters using embedded networks

Record Deduplication (RD) aims to identify instances that represent the same real-world entity in data repositories. In the government environment, the RD process facilitates the identification of irregularities and reduces the consumption of computing resources in data integration tasks. In this context, we propose a scalable, effective and efficient platform, called DedupeGov, for integrating large data repositories (i.e., with large volumes of data, in the order of millions of records) to unify duplicate entities from multiple and different sources. Our experimental results indicate a 21.8% of reduction in the number of records of the original repository with 99% of precision and 95% of recall when identifying duplicate records. In addition, our platform was capable of building more complete records, eliminating at least 32% of records with null attributes. Furthermore, our solution is very efficient and scalable for large volumes of data, deduplicating a repository of almost 400 million records in around one hour, besides being easy to generalize to different types of entity.

Deduplicating Large Volumes of Data from Natural and Legal Entities in the Governmental Field

Recognition of Images of Korean Characters Using Embedded Networks

The healthcare industry, through digitization, is trying to achieve interoperability, but has not been able to achieve complete Health Information Exchange (HIE). One of the major challenges in achieving this is the inability to accurately match patient data. Mismatching of patient records can lead to improper treatment which can prove to be fatal. Also, the presence of duplicate overheads has caused inaccessibility to crucial information in the time of need. Existing solutions to patient matching are both time-consuming and non-scalable. This paper proposes a framework, namely, Electronic Patient Matching System (EPMS), which attempts to overcome these barriers while achieving a good accuracy in matching patient records. The framework encodes the patient records using variational autoencoder and amalgamates them by performing locality sensitive hashing on an Apache spark cluster. This makes the process faster and highly scalable. Furthermore, a fuzzy matching of the records in each block is performed using Levenshtein distances to identify the duplicate patient records. Experimental investigations were performed on a synthetically generated dataset consisting of 44555 patient records. The proposed framework achieved a matching accuracy of 81.15% on this dataset.

EPMS: A Framework for Large-Scale Patient Matching

Optical Character Recognition (OCR) of Tamil characters is harder, due to the variety, compared to that of Latin characters. We propose a new technique to perform optical character recognition of printed Tamil characters using a hierarchical Convolutional Neural Network (CNN) which recognises the similarities between characters, while identifying the differences between the fine-grained characters. The performance of the developed system is comparable to the stateof-the-art techniques for OCR.

Harish Ravi

Papers

EPMS: A Framework for Large-Scale Patient Matching

Hierarchical OCR for printed Tamil text