Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN) which are one of the recent techniques adopted from the deep learning community. We experimented the ISIDCHAR database provided by (Information Sharing Index) ISI, Kolkata and V2DMDCHAR database with six different architectures of DCNN to evaluate the performance and also investigate the use of six recently developed adaptive gradient methods. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.

As people have become more aware of their actions and how they affect their surroundings, they have realized the dire state of the environment. As a result, the recycling movement has gained momentum as a measure to save it. Contemporarily, the recycling industry has not seen a major shift and the problems that existed decades earlier persist. Trash classification is at the core of these problems, because if you can’t classify it, obviously you can’t recycle it. Manual classification often leads to misclassification as humans classify or judge things based on their experiences, knowledge, and not just absolute facts. Additionally, if the waste to be sorted is toxic, being in direct contact may be physically harmful to the people involved. Until a solution is found for this problem, the recycling industry won’t be on par with the rise in recycling culture. Thus, this is the problem we have set out to solve; this paper proposes a Deep Learning model based on EfficientNet Architecture that can classify different kinds of trash with an accuracy high enough to make it a viable solution for the industry while using a comparatively lower number of parameters than existing methods. We achieved an accuracy of 98% on the TrashNet dataset, the standard data for Trash classification, and outperformed all existing models. Additionally, as no large dataset with a varied set of trash images was present, we created a new dataset of 8135 images by combining various datasets and standardized them, achieving a classification accuracy of 92.87% with EfficientNet B3.

In this manuscript we recognize the handwritten Devnagari numerals. In our implementation we have used density and background directional distribution features for the zones, in which we divided the numeral samples already. We used the normalized images of samples of varying sizes of 32*32, 40*40 and 48*48. We divided these normalized images into 4*4 (16), 5*5 (25) and 6*6 (36) zones respectively to compute the features for each zone. In all the cases each zone is of size 8*8 pixels. Each zone contains 9 features consisting of one density feature and 8 backgrounds directional distribution features. The zonal density feature is computed by dividing the number of foreground pixels in each zone by total number of pixels in the zone i.e. 64. The other 8 features are based on directional distribution values of background in eight directions. These directional values are computed for each foreground pixel by summing up the value corresponding to neighbouring background pixels given in the specific mask for each direction. For each direction these directional distribution features are summed up for all pixels in each zone. Thus numbers of features finally used for recognition are 144, 225 and 324 for samples of respective sizes in increasing order. For classification purpose we have used SVM classifier with RBF kernel. Our dataset of handwritten Devnagari numerals used is provided by Indian Statistical Institute (ISI), Kolkata. Training data size is 18783 and testing data size is 3763, totally 22546. The optimum 5-fold cross validation accuracies of training data obtained for varying sizes of samples in increasing order are 98.76%, 98.91% and 98.94% respectively. By observing the cross validation results it is conclusive that at the cost of increasing the features size there is only minute increases in the performance. So we recommend 144 sized feature vector to recognize testing samples. The testing accuracy by using 144 features for 32*32 normalized samples observed is 98.51% which is prominent and cost-efficient.

This manuscript is focus on the utilization of object detection algorithm GLAC (Gradient Local Auto-Correlation) for the handwritten character recognition (HCR) problem. HOG and SIFT are already used in this (HCR) field except GLAC which produced good results than HOG and SIFT for object detection problem like human in images, pedestrian detection and image patch matching. This paper utilized GLAC algorithm to recognize the handwritten Devanagari characters. GLAC applied on two handwritten Devanagari databases, ISIDCHAR and V2DMDCHAR. The images of databases are also normalized with and without preserving aspect ratio. Using GLAC method and SVM classifier, the best results obtained on ISIDCHAR and V2DMDCHAR are 93.21%, 95.21 % respectively that justified the utilization of GLAC algorithm for character recognition problem.

The recognition of handwritten similar shape characters are still a challenging problem in HOCR system. Almost entire scripts are suffering with this kind of problem. This manuscript is considered the similar shape character problem in Devanagari script. An algorithm is proposed first to estimate the similar character pairs in Devanagari Script and 7 pairs are identified by investigating the confusion matrix. Similar shape characters have very minor difference in shape that's why at the time of recognition (classification) phase, classifier is being confused with another similar shape characters. This problem can be solved by estimate that minor difference called critical region in the similar shape characters and used critical region to extract the more features before classification phase. The critical region is estimated by Fisher discrimination function. A new kind of masking techniques used to extract the features from ISIDCHAR (standard Devanagari database) and 96.58 % recognition rate is obtained finally after getting the 81.94% improvement in similar character recognition.