Does color modalities affect handwriting recognition? An empirical study on Persian handwritings using convolutional neural networks

Most of the methods on handwritten recognition in the literature are focused and evaluated on Black and White (BW) image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), as eye simulator, we investigate to see whether color modalities of handwritten digits and words affect their recognition accuracy or speed? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten databases that have all three color modalities of handwritings. To answer this question, we selected 13,330 isolated digits and 62,500 words from a novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected datasets are divided into training, validation, and testing sets. Afterwards, similar conventional CNN models are trained with the training samples. While the experimental results on the testing set show that CNN on the BW digit and word images has a higher performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modalities. Also, comparisons of training times in three color modalities show that recognition of handwritten digits and words in BW images using CNN is much more efficient

Automatic segmentation and recognition of unconstrained handwritten numeral strings

Segmentation and recognition of handwritten numeral strings is a very interesting and challenging problem in pattern recognition. It also has a lot of important applications such as: postal code recognition, bank check processing; tax form reading, etc. In this thesis, a new system for the segmentation and recognition of unconstrained handwritten numeral strings is proposed. The system uses a combination of foreground and background features for the segmentation of touching numerals in strings. The method introduces new algorithms for the traversal of top and bottom foreground and background skeletons, and top and bottom contours of numerals. Then; it tries to locate all feature points on these skeletons and contours and alternatively match feature points from top to bottom (or bottom to top) of the images to build all possible candidate segmentation paths (so-called segmentation hypotheses). A novel genetic representation scheme is utilized in order to represent the space of all possible segmentation hypotheses. In order to improve searching and evolution of segmentation hypotheses and facilitate finding the ones with the highest confidence values of segmentation and recognition, this genetic framework utilizes contextual knowledge extracted from string images. A novel evaluation scheme based on segmentation and recognition scores is introduced in order to improve the evaluation of segmentation hypotheses and to enhance the outlier resistance of the system. In order to improve stability and plasticity of our system in the learning and recognition of numerals, a new algorithm for clustering of handwritten digits based on their shapes is proposed. Also, in order to improve the searching power of our system and its convergence, a new evolutionary algorithm based on genetic particle swarm optimization (GBPSO) is proposed. Numerous experiments using images from well known databases of handwritten numeral strings such as CENPARMI, NIST NSTRING SD19, and our newly created databases of Farsi/Arabic numerals have been conducted in order to evaluate the performance of the proposed method. Experiments have shown that proper use of contextual knowledge in segmentation; evaluation and search greatly improves the overall performance of the system. This system shows superior results compared with those reported in the literature

Association between the Dietary Inflammatory Index with gallstone disease: finding from Dena PERSIAN cohort

Objective The Dietary Inflammatory Index (DII) is a documented nutritional tool for assessing diet-induced inflammation that has been linked to various diseases/outcomes. The association between DII and gallstone disease (GSD) is yet to be explored. The objective of this study was to examine the association between DII and GSD.Design This cross-sectional study was conducted using the baseline phase data of the Dena PERSIAN cohort. The analysed data included demographic information, lifestyle variables, body mass index, diabetes and fatty liver history, and laboratory test results. The 113-item Food Frequency Questionnaire was used to estimate the dietary intake of participants and quantify the inflammatory potential of the individual’s diet. DII score was analysed as a continuous and quartiles variables. Univariable and multivariate logistic regressions were used to investigate the relationship between GSD and DII scores .Results Out of 3626 individuals entering the study, 173 (4.77%) had GSD. The median DII was −0.08 (IQR=0.18). In the unadjusted model, the odds of having GSD were significantly higher in the first and second quartiles of DII (anti-inflammatory diet) than in higher quartiles (proinflammatory diet). In the adjusted model, the odds of having GSD in the third and fourth quartiles of DII scores compared with the first quartile were OR=0.59 (95% CI 0.36 to 0.95) and OR 0.51 (95% CI 0.30 to 0.84), respectively.Conclusion The results of this study suggest that a proinflammatory diet is associated with a reduced chance of GSD. However, longitudinal studies are needed to examine the causal association

STATE-OF-THE-ART IN FARSI SCRIPT RECOGNITION

In this paper, a brief history of the evolution of Farsi (Persian) script is presented, including how the Farsi alphabet was derived from the Arabic alphabet. Important features, similarities and dissimilarities between Farsi and Arabic scripts, from the Optical Character Recognition (OCR) point of view are discussed. Also, a brief review of some of the state-of-the-art techniques in off-line and on-line Farsi script recognition and segmentation, as well as challenges ahead are briefly presented. 1

Study of Serologic Response Rate to Pertussis after Administration of the Third Dose of Pentavalent Vaccine in Children 12 Months Old in Karaj City, Iran

Background: After substitution of Pentavalent vaccine with diphtheria, tetanus, pertussis (DTP) in the Iranian National Vaccination program with 3 Pentavalent (three times vaccination with Pentavalent vaccine at months 2, 4, and 6) in 2014 and the lack of published research in the field of immunogenicity of pertussis component of this vaccine, the efficacy of pertussis vaccine was studied 6 months after the last dose of Pentavalent vaccine in Iranian infants.  Materials and Methods: Five hundred blood samples were collected from healthy one-year-old children who attended 18 health care centers of Karaj, Iran for routine vaccination selected by cluster sampling (2016). Sampling checklists contained demographic information and risk factors. The blood samples were sent to the laboratory for determination of Immunoglobulin G (IgG) and IgA anti-pertussis antibody titer by ELISA method. Data were analyzed by STATA software (version 14.0). Results: 82.7% (n=413) of children (95% confidence interval [CI]: 79.49-86.11) had IgG titer less than 16 IU/ml against pertussis (no immune response), and 17.3% (n=87) had equal or greater than 16 IU/ml IgG titer against pertussis (95% CI: 13.89-20.51). IgA titer against pertussis was less than 8U/ml in all cases. Anti-pertussis IgG geometric mean titer (GMT) was 15.80 U/ml (95% CI: 15.26-16.36), and IgA GMT was 6.26 U/ml (95% CI: 6.22-6.30). There was not a significant correlation between titer of pertussis antibody and demographic factors. Conclusion: Based on low IgG titer in vaccinated children, immunogenicity of pentavalent vaccine in Iranian children needs more investigation. In this study, 100 % of children had negative serologic response (Ig