TiCRL: Textual Image Classification with Reinforcement Learning-Based Curriculum Learning

Textual image classification remains challenging due to thediversity of document layouts and textual structures. We propose TiCRL(Textual Image Classification with Reinforcement Learning-based Cur-riculum Learning), which combines a text-independent difficulty mea-surer with an RL-based training scheduler. The measurer estimates sam-ple difficulty using visual-textual features and loss signals without re-quiring OCR, while the RL agent dynamically adapts the curriculumbased on the learner’s state. TiCRL substantially improves both accu-racy and data efficiency. On a lightweight CNN trained from scratch,it achieves 83.67% accuracy using only 82% of the training data, out-performing curriculum learning baselines by up to 13.8%p. Applied toa strong transformer baseline (DiT), TiCRL reaches 93.02% accuracyusing only 58% of the data, surpassing transformer baselines trained on80%. The learned curriculum policy generalizes to new datasets and ar-chitectures without retraining, demonstrating that RL-based curriculumlearning is an effective and efficient training strategy for textual imageclassification.