Language Identification using Markov Chains, Hidden Markov Models, and Neural HMMs. Explores probabilistic and deep learning methods for sequence modeling, with ablation studies on context, hidden states, and optimization.

📝 Deep learning OCR system for handwritten text recognition using CTC loss & CNN-BiLSTM architecture. Features include image preprocessing with adaptive frame splitting, time-distributed convolutional layers for feature extraction, bidirectional LSTM for sequence modeling, batch normalization for training stability. LER & SER performance metrics

Linear-time sequence modeling that replaces attention's O(n²d) complexity with O(nd) summation-based aggregation. Demonstrates constraint-driven emergence: how functional representations can develop from optimization pressure and architectural constraints alone, without explicit pairwise interactions.