Abstract

Handwritten text recognition in degraded documents remains a major challenge in document image analysis due to factors such as noise, handwriting variability, uneven lighting, faded ink, and physical distortions in historical or low-quality scans. Traditional OCR methods often perform poorly under these circumstances. To improve recognition accuracy, this research proposes a strong architecture that combines a fixed Convolutional Recurrent Neural Network (CRNN)with refined image prose. The preprocessing pipeline includes grayscale normalization, adaptive thresholding, noise filtering (e.g., median and Gaussian smoothing), and morphological operations like dilation and erosion to improve image clarity while preserving critical handwriting features. These refined images are then processed by a CRNN architecture, comprising convolutional layers for spatial feature extraction, bidirectional recurrent layers (LSTM) for sequence modelling, and a Connectionist Temporal Classification (CTC) loss for transcription without character-level segmentation. The addition of preprocessing models reduces the rate of transmitter rate (CER) and word error speed (WER), increasing training stability and flexibility. Our study forms a base line to detect handwriting, such as creating old manuscripts digital and analysing multilingual documents under complex, real -world conditions, which are both effective and expandable.

Authors

M. Antony Sheela, J. Jencewin, S.L. Rinu
St. Xavier’s Catholic College of Engineering, India

Keywords

Handwriting Recognition, CRNN, Preprocessing, Degraded Documents, CTC Loss, Document Analysis, OCR Enhancement, Historical Digitization