Automation of the recognition of Optical Character Recognition (OCR) in handwritten documents

Handwritten document analysis has gained significant attention in recent years due to its relevance in various fields such as forensic document examination, historical document preservation, and personal identity verification. This paper presents an innovative approach for the recognition of writing styles in handwritten documents, focusing on analysis at the levels of documents, lines, and words. At the document level, our methodology employs advanced pattern recognition algorithms to capture holistic features, considering overall layout, spacing, and formatting. This enables the identification of distinctive characteristics that define an individual's writing style consistently across multiple pages. Moving to the line level, the proposed approach integrates spatial and temporal information to analyze the dynamics of handwriting within a single line. By exploring variations in stroke thickness, slant, and curvature, the model can differentiate between different writing styles, even when presented with subtle variations. At the word level, a detailed analysis of individual characters and their spatial relationships is conducted. Our system utilizes machine learning techniques to recognize unique patterns within the structure of words, considering factors such as letter connectivity, spacing, and size. To achieve robust recognition, a combination of deep learning and traditional image processing methods is employed. The model is trained on a diverse dataset, encompassing various writing styles, languages, and document types. Experimental results demonstrate the effectiveness of the proposed approach in accurately identifying and distinguishing writing styles across different levels of granularity.