  Shapes and texture image recognition usage is an essential branch of pattern
recognition. It is made up of techniques that aim at extracting information
from images via human knowledge and works. Local Binary Pattern (LBP) ensures
encoding global and local information and scaling invariance by introducing a
look-up table to reflect the uniformity structure of an object. However, edge
direction matrixes (EDMS) only apply global invariant descriptor which employs
first and secondary order relationships. The main idea behind this methodology
is the need of improved recognition capabilities, a goal achieved by the
combinative use of these descriptors. This collaboration aims to make use of
the major advantages each one presents, by simultaneously complementing each
other, in order to elevate their weak points. By using multiple classifier
approaches such as random forest and multi-layer perceptron neural network, the
proposed combinative descriptor are compared with the state of the art
combinative methods based on Gray-Level Co-occurrence matrix (GLCM with EDMS),
LBP and moment invariant on four benchmark dataset MPEG-7 CE-Shape-1, KTH-TIPS
image, Enghlishfnt and Arabic calligraphy . The experiments have shown the
superiority of the introduced descriptor over the GLCM with EDMS, LBP and
moment invariants and other well-known descriptor such as Scale Invariant
Feature Transform from the literature.
