This research presents a novel post-processing method for convolutional neural networks (CNNs) in character recognition, specifically designed to handle inconsistencies and irregularities in character shapes. Convolutional Neural Networks (CNNs) are powerful tools for recognizing and learning character shapes directly from source images, making them well-suited for recognition of characters that contain inconsistencies in their shapes. However, when applied to multi-object detection for character recognition, CNNs require post-processing to convert the recognized characters into code sequences, which has so far limited their applicability. The developed method solves this problem by directly post-processing the inconsistent characters identified by the convolutional neural model into labels corresponding to the source image. An experiment with real pharmaceutical packaging images demonstrates the functionality of the method, showing that it can handle different numbers of characters and labels effectively. As a scientific contribution to the fields of imaging and deep learning, this research opens new possibilities for future studies, particularly in the development of more accurate and efficient multi-object character recognition with post-processing and their application to new areas.