A Computational Framework for Ink Bleed Suppression in Handwritten Document Images

Shrikant Baronia

Abstract

Ink-bleed is a common form of degradation in handwritten document images, where ink from the reverse side or adjacent lines seeps into the visible content, impairing readability and adversely affecting downstream tasks such as Optical Character Recognition (OCR). Traditional image processing techniques often fall short in preserving the fine structure of handwritten strokes while removing bleedthrough noise.

This thesis presents a machine learning-based computational framework for ink bleed suppression using a layer separation approach. The proposed method models the document image as a combination of content and bleed-through layers and employs a Dual Layer Markov Random Field (DL-MRF) architecture to learn the separation in a supervised setting. A synthetic dataset with controlled bleed artifacts was constructed for training, along with augmentation strategies to simulate various bleed intensities and patterns.

The model was evaluated on both synthetic and real handwritten document images. The results demonstrate significant improvement over traditional filtering techniques and baseline learning models, preserving content integrity while effectively reducing ink bleed.

This work contributes towards robust document image restoration, with applications in digital archiving, historical manuscript preservation, and pre-processing pipelines for handwriting analysis systems.

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