An innovative network based on double receptive field and Recursive Bi-directional Long Short-Term Memory

Sequence recognition of natural scene images has always been an important research topic in the field of computer vision. CRNN has been proven to be a popular end-to-end character sequence recognition network. However, the problem of wide characters is not considered under the setting of CRNN. The CRNN is less effective in recognizing long dense small characters. Aiming at the shortcomings of CRNN, we proposed an improved CRNN network, named CRNN-RES, based on BiLSTM and multiple receptive fields. Specifically, on the one hand, the CRNN-RES uses a dual pooling core to enhance the CNN network’s ability to extract features. On the other hand, by improving the last RNN layer, the BiLSTM is changed to a shared parameter BiLSTM network using recursive residuals, which reduces the number of network parameters and improves the accuracy. In addition, we designed a structure that can flexibly configure the length of the input data sequence in the RNN layer, called the CRFC layer. Comparing the CRNN-RES network proposed in this paper with the original CRNN network, the extensive experiments show that when recognizing English characters and numbers, the parameters of CRNN-RES is 8197549, which decreased 133,752 parameters compare with CRNN. In the public dataset ICDAR 2003 (IC03), ICDAR 2013 (IC13), IIIT 5k-word (IIIT5k), and Street View Text (SVT), the CRNN-RES obtain the accuracy of 96.90%, 89.85%, 83.63%, and 82.96%, which higher than CRNN by 1.40%, 3.15%, 5.43%, and 2.16% respectively.

The Interaction Between Area and Production of Food Grain Crops in India : An Empirical Evidence from ADRL Bounds Test Cointegration

This paper demonstrates a significant long-run relationship between area and productions of Food grain crops grown in India during the period 1950-2018. Stability of the estimated model parameters are studied . To assess the consistency of the model parameters the cumulative sum of recursive residuals test and the cumulative sum of recursive residuals squares tests are used.Additionally, cointegration equations such as the Fully Modified Ordinary Least Square Dynamic Ordinary Least Squares, and Canonical Cointegration Regression are applied to check the long-run elasticities in the concerned relationship.

An Innovative Network based on Double Receptive Field and Recursive Bi-directional Long Short-Term Memory

In the scenes of character recognition, this paper studies the influence of network structure and parameters of “An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition (CRNN) ” on the recognition results in detail, and proposes an improved CRNN network based on double receptive field and recursive Bi-directional Long Short-Term Memory(BILSTM), which is named “An innovative network based on double receptive field and Recursive Bi-directional Long Short-Term Memory༈CRNN_RES༉”. In the CRNN_RES network, the innovations of this paper are adjusting the structure of CNN to enhance the feature extraction ability of the CNN network and using the shared parameter BILSTM network with recursive residuals to reduce the number of network parameters and improve the accuracy of the model prediction. In fact, the number of parameters of CRNN_RES network proposed in this paper is 7148325, which is 1182976 fewer than that of CRNN. On the same open datasets:ICDAR 2003༈IC03༉,ICDAR 2013༈IC13༉༌IIIT 5k-word༈IIIT5k༉, and Street View Text (SVT), the proposed method achieves 96.90%, 89.85%, 83.63%, and 82.96% recognition accuracy, which are higher than that of CRNN 1.40%, 3.15%, 5.43% ༌and 2.16%.

Product Newness, Low Competition, Recent Technology, and Export Orientation as Predictors for Entrepreneurial Growth Aspirations

This study examines the contribution of how product newness, low competition, recent technology, and export orientation affect entrepreneurial growth aspirations moderated by financial capital. Based on a Global Entrepreneurship Monitor (GEM) sample of 512 Mexican new entrepreneurs, we use a hierarchical regression model to study the independent and interaction effects between these variables, and we apply a Chow breakpoint test and a CUSUMSQ (cumulative sum of squares of recursive residuals) test to analyze structural change and robustness. Our results suggest that achieving higher educational levels, acquiring recent technology, and product newness slightly increase the entrepreneurial growth ambition of the firm, and that financial capital positively moderates the impact of product newness and recent technology on growth aspirations. Besides this, we show that the interaction effect of financial capital with low competition and export activity on their growth aspirations is not crucial, and business angles tend to finance, primarily when the firm exports new products and services are facing a reduced number of competitors.

The Determinants of Money Demand in Jordan: ARDL Approach

The current study examined the relationship between real money demand (M2) and its determinants represented by real gross domestic product, real interest rate, inflation rate and budget deficit in Jordan for the period (2000Q1-20018Q1). The study used unit root test, Autoregressive Distributive Lag (ARDL), cointegration and long run, bound test to examine the study hypotheses. ARDL cointegration equation and ARDL Bound test show that there is a long run relationship between money demand M2 and its determinants, real interest rate, inflation rate, budget deficit and real gross domestic product. The short run ARDL results shows that the past period of money demand has a negative and significant impact on money demand, while inflation rate and Gross domestic product have a positive and significant impact on money demand in Jordan. The long run ARDL results show that the inflation rate, real gross domestic product and budget deficit have a positive long run relationship with money demand (M2)and Its impact on (M2 ) is positive and statistically significant at 1 percent level, while interest rate has a negative and significant impact on Money demand (M2 ). Inflation rate, real gross domestic product, budget deficit and interest rate are good determinants for money demand M2. The cumulative sum (CUSUM) of recursive residuals and cumulative sum of squares (CUSUMQ) of recursive residuals confirm that the estimated money demand M2 model is stable.