Printed text images (PIs) play an important role in human society for communication purposes. So, it is very much essential for the categorization of PIs into several types in a better way. In this work, a machine intelligent (MI) based approach is proposed for the classification ofPIs into several categories such as Odia text printed images (OPIs) MSRA text printed images (MTPIs).The proposed approach is focused on the stacking (hybridization) of LogisticRegression(LRG) andNeuralNetwork(NNT)methods tocarryout suchclassification.The proposed method is compared with other machine learning (ML) based methods such as AdaBoost (ADB), RandomForest (RFS), Decision Tree (DTR), K-Nearest Neighbor (KNNH), Support Vector Machine (SVMN), LRG and NNT for performance analysis. The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 220PIs having200 numbers of MTPIs type are taken from the Kaggle source, and 20 numbers of OPIs are takenfrom the other sources. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results interms of CA, F1, PR and RC as compared to other ML based methods such as ADB, RF, DTR, KNNH, SVMN, LRG and NNT.

Odia Text Printed Images, Machine Intelligence, Classification Accuracy, F1, Precision, Recall