Hand written character recognition is widely used in modern research. Recognize the characters from an image has always been active research area in the computer vision community. Apart from English, there are several regional languages available worldwide. In India there are 22 official languages, and Bengali is one of them. Bengali script has its own writing pattern. Like any other language number system, Bengali numeral system has ten different digits to indicate 0-9. It has its own alphabets and numerals. In this paper, Histogram of oriented gradient (HOG) and color histogram for selection of features algorithm is proposed. HOG are used as the feature set to represent each digit sample at the feature space and Support Vector Machine (SVM) used to produce the output from input image. Proposed algorithm is efficient and gives an accuracy of 98.05 on CMATERDB3.1.1 dataset. This type of numeric image recognition can be use in the post offices to acknowledge the pin code written in Bengali.

Purpose: The purpose of this paper is to develop an automated phase segmentation model from complex microstructure. The mechanical and physical properties of metals and alloys are influenced by their microstructure, and therefore the investigation of microstructure is essential. Coexistence of random or sometimes patterned distribution of different microstructural features such as phase, grains and defects makes microstructure highly complex, and accordingly identification or recognition of individual phase, grains and defects within a microstructure is difficult.Design/methodology/approach: In this perspective, computer vision and image processing techniques are effective to help in understanding and proper interpretation of microscopic image. Microstructure-based image processing mainly focuses on image segmentation, boundary detection and grain size approximation. In this paper, a new approach is presented for automated phase segmentation from 2D microstructure images. The benefit of the proposed work is to identify dominated phase from complex microstructure images. The proposed model is trained and tested with 373 different ultra-high carbon steel (UHCS) microscopic images.Findings: In this paper, Sobel and Watershed transformation algorithms are used for identification of dominating phases, and deep learning model has been used for identification of phase class from microstructural images.Originality/value: For the first time, the authors have implemented edge detection followed by watershed segmentation and deep learning (convolutional neural network) to identify phases of UHCS microstructure.