Binarization and cleanup of handwritten text from carbon copy medical form images

This paper presents a methodology for separating handwritten foreground pixels, from background pixels, in carbon copied medical forms. Comparisons between prior and proposed techniques are illustrated. This study involves the analysis of the New York State (NYS) Department of Health (DoH) Pre-Hospital Care Report (PCR) [Western Regional Emergency Medical Services, Bureau of Emergency Medical Services, New York State (NYS) Department of Health (DoH), Prehospital Care Report v4.] which is a standard form used in New York by all Basic and Advanced Life Support pre-hospital health care professionals to document patient status in the emergency environment. The forms suffer from extreme carbon mesh noise, varying handwriting pressure sensitivity issues, and smudging which are further complicated by the writing environment. Extraction of handwriting from these medical forms is a vital step in automating emergency medical health surveillance systems.     

Automatic recognition of handwritten medical forms for search engines

A new paradigm, which models the relationships between handwriting and topic categories, in the context of medical forms, is presented. The ultimate goals are: (1) a robust method which categorizes medical forms into specified categories, and (2) the use of such information for practical applications such as an improved recognition of medical handwriting or retrieval of medical forms as in a search engine. Medical forms have diverse, complex and large lexicons consisting of English, Medical and Pharmacology corpus. Our technique shows that a few recognized characters, returned by handwriting recognition, can be used to construct a linguistic model capable of representing a medical topic category. This allows (1) a reduced lexicon to be constructed, thereby improving handwriting recognition performance, and (2) PCR (Pre-Hospital Care Report) forms to be tagged with a topic category and subsequently searched by information retrieval systems. We present an improvement of over 7% in raw recognition rate and a mean average precision of 0.28 over a set of 1,175 queries on a data set of unconstrained handwritten medical forms filled in emergency environments. This work was supported by the National Science Foundation.     

uWave: Accelerometer-based personalized gesture recognition and its applications

The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures. We evaluate uWave using a large gesture library with over 4000 samples for eight gesture patterns collected from eight users over one month. uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. We also present applications of uWave in gesture-based user authentication and interaction with 3D mobile user interfaces. In particular, we report a series of user studies that evaluates the feasibility and usability of lightweight user authentication. Our evaluation shows both the strength and limitations of gesture-based user authentication.     

Synthesis and characterisation of neem leaf extract, 2, 3‐dehydrosalanol and quercetin dihydrate mediated silver nano particles for therapeutic applications

The utility of green silver nanoparticles (AgNPs) in veterinary medicine is steadily increasing as they have many therapeutic applications against pathogens and arthropods of livestock. In this study, green AgNPs using neem (N‐AgNPs), 2,3‐dehydrosalanol (2,3‐DHS‐AgNPs) and quercetin dihydrate (QDH‐AgNPs) were synthesised and characterised. Synthesised compounds were characterised by UV‐Vis spectroscopy and the peak absorbance was recorded at 370 nm for neem extract. For N‐AgNPs, 2,3‐DHS‐AgNPs and QDH‐AgNPs, the maximum absorbance peaks were at 430, 230 and 220 nm, respectively. The FTIR analysis confirmed the synthesis of green AgNPs. The XRD pattern of N‐AgNPs showed the peaks corresponding to whole spectra of 2 θ values ranging from 10–80. The relatively higher intensity of (111, 222) planes in face centred cubic crystalline structure supports the formation of synthesised AgNPs. In DLS analysis, the hydrodynamic diameter of neem leaf extract was found to be 259.8 nm, followed by 5.3, 6.7 and 261.8 nm for 2,3‐DHS‐AgNPs, N‐AgNPs and QDH‐AgNPs, respectively. Based on the transmission electron microscopy and scanning electron microscopy image analyses, confirmed the formation of N‐AgNPs, 2,3‐DHS‐AgNPs and QDH‐AgNPs. These eco‐friendly phyto‐AgNPs may be of use as an effective alternative to chemical control methods against the arthropods of livestock.Inspec keywords: nanoparticles, silver, nanomedicine, biomedical materials, nanofabrication, Fourier transform infrared spectra, ultraviolet spectra, visible spectra, X‐ray diffraction, light scattering, transmission electron microscopy, scanning electron microscopy, aggregation, veterinary medicineOther keywords: 2,3‐dehydrosalanol mediated silver nanoparticles, quercetin dihydrate mediated silver nanoparticles, therapeutic applications, green silver nanoparticles, veterinary medicine, Azadirachta indica, UV‐visible spectroscopy, Fourier transformed infrared analysis, X‐ray diffraction, (111) planes, (222) planes, face centred cubic crystalline structure, dynamic light scattering, hydrodynamic diameter, aqueous neem leaf extract, transmission electron microscopy, hexagonal shape, pencil head shape, cuboid shape, scanning electron microscopy, aggregation, arthropod infesting livestock, Ag, in‐vivo antiectoparasitic activity, in‐vitro antiectoparasitic activity     

Unconstrained handwritten document retrieval

With the ever-increasing growth of the World Wide Web, there is an urgent need for an efficient information retrieval system that can search and retrieve handwritten documents when presented with user queries. However, unconstrained handwriting recognition remains a challenging task with inadequate performance thus proving to be a major hurdle in providing robust search experience in handwritten documents. In this paper, we describe our recent research with focus on information retrieval from noisy text derived from imperfect handwriting recognizers. First, we describe a novel term frequency estimation technique incorporating the word segmentation information inside the retrieval framework to improve the overall system performance. Second, we outline a taxonomy of different techniques used for addressing the noisy text retrieval task. The first method uses a novel bootstrapping mechanism to refine the OCR’ed text and uses the cleaned text for retrieval. The second method uses the uncorrected or raw OCR’ed text but modifies the standard vector space model for handling noisy text issues. The third method employs robust image features to index the documents instead of using noisy OCR’ed text. We describe these techniques in detail and also discuss their performance measures using standard IR evaluation metrics.     

Synthetic handwritten CAPTCHAs

CAPTCHAs (completely automated public Turing test to tell computers and humans apart) are in common use today as a method for performing automated human verification online. The most popular type of CAPTCHA is the text recognition variety. However, many of the existing printed text CAPTCHAs have been broken by web-bots and are hence vulnerable to attack. We present an approach to use human-like handwriting for designing CAPTCHAs. A synthetic handwriting generation method is presented, where the generated textlines need to be as close as possible to human handwriting without being writer-specific. Such handwritten CAPTCHAs exploit the differential in handwriting reading proficiency between humans and machines. Test results show that when the generated textlines are further obfuscated with a set of deformations, machine recognition rates decrease considerably, compared to prior work, while human recognition rates remain the same.     

Holistic recognition of handwritten character pairs

Researchers have thus far focused on the recognition of alpha and numeric characters in isolation as well as in context. In this paper we introduce a new genre of problems where the input pattern is taken to be a pair of characters. This adds to the complexity of the classification task. The 10 class digit recognition problem is now transformed into a 100 class problem where the classes are {00,…, 99}. Similarly, the alpha character recognition problem is transformed to a 26×26 class problem, where the classes are {AA,…, ZZ}. If lower-case characters are also considered the number of classes increases further. The justification for adding to the complexity of the classification task is described in this paper. There are many applications where the pairs of characters occur naturally as an indivisible unit. Therefore, an approach which recognizes pairs of characters, whether or not they are separable, can lead to superior results. In fact, the holistic method described in this paper outperforms the traditional approaches that are based on segmentation. The correct recognition rate on a set of US state abbreviations and digit pairs, touching in various ways, is above 86%.     

A Mouse-Based Method to Monitor Wheat Allergens in Novel Wheat Lines and Varieties Created by Crossbreeding: Proof-of-Concept Using Durum and A. tauschii Wheats

Wheat allergies are potentially life-threatening because of the high risk of anaphylaxis. Wheats belong to four genotypes represented in thousands of lines and varieties. Monitoring changes to wheat allergens is critical to prevent inadvertent ntroduction of hyper-allergenic varieties via breeding. However, validated methods for this purpose are unavailable at present. As a proof-of-concept study, we tested the hypothesis that salt-soluble wheat allergens in our mouse model will be identical to those reported for humans. Groups of Balb/cJ mice were rendered allergic to durum wheat salt-soluble protein extract (SSPE). Using blood from allergic mice, a mini hyper-IgE plasma bank was created and used in optimizing an IgE Western blotting (IEWB) to identify IgE binding allergens. The LC-MS/MS was used to sequence the allergenic bands. An ancient Aegilops tauschii wheat was grown in our greenhouse and extracted SSPE. Using the optimized IEWB method followed by sequencing, the cross-reacting allergens in A. tauschii wheat were identified. Database analysis showed all but 2 of the durum wheat allergens and all A. tauschii wheat allergens identified in this model had been reported as human allergens. Thus, this model may be used to identify and monitor potential changes to salt-soluble wheat allergens caused by breeding.     

Effect of nano additives (titanium and zirconium oxides) and diethyl ether on biodiesel-ethanol fuelled CI engine

The present work is dedicated to the comparative experimental study of biodiesel-ethanol blends in a compression ignition engine using TiO₂ (Titanium oxide) nanoparticle, ZrO₂ (Zirconium oxide) nanoparticle and DEE (Diethyl ether) additives. The test fuels used are a blend of biodiesel (80%) -ethanol (20%) (denoted as BE), a blend of BE with 25 ppm Titanium oxide nanoparticle (denoted as BE-Ti), a blend of BE with 25 ppm Zirconium oxide nanoparticle (denoted as BE-Zr) and a blend of BE with 50 ml Diethyl ether (denoted as BE-DEE). Addition of nanoparticles increases the oxidation rate, reduces the light-off temperature and creates large contact surface area with the base fuel thereby enhancing the combustion with minimal emissions. Experimental results shown that addition of Titanium nanoparticles increased NOx, HC and smoke with lowered BSFC and CO. Whereas addition of Zirconium nanoparticles increases BSFC and HC emissions with lowered CO, CO₂ and smoke emissions in comparison with BE blends. DEE addition to BE blends improved the heat release rate and increased HC, CO emissions were observed with lowered BSFC, NOx and smoke. Simultaneous reduction of NOx and smoke indicates the effect of DEE on Low temperature combustion (LTC).