A HYBRID PARAGRAPH-LEVEL PAGE SEGMENTATION

Automatic transformation of paper documents into electronic forms requires geometrydocument layout analysis at the rst stage. However, variations in character font sizes, text-linespacing, and layout structures have made it dicult to design a general-purpose method. Page seg-mentation algorithms usually segment text blocks using global separation objects, or local relationsamong connected components such as distance and orientation, but typically do not consider infor-mation other than local component's size. As a result, they cannot separate blocks that are veryclose to each other, including text of dierent font sizes and paragraphs in the same column. Toovercome this limitation, we proposed to use both separation objects at the whole page level andcontext analysis at text-line level to segment document images into paragraphs. The introduced hy-brid paragraph-level page segmentation (HP2S) algorithm can handle dicult cases where the purelytop-down and bottom-up approaches are not sucient to separate. Experimental results on the testset ICDAR2009 competition and UW-III dataset shown that our algorithm boost the performancesignicantly comparing to the state of the art algorithms

Enhancing optical character recognition: Efficient techniques for document layout analysis and text line detection

In recent years, automatic document and text analysis has gained significant importance, driven by advancements in optical character recognition (OCR) technology and the need for efficient processing of large volumes of printed or handwritten documents. This article specifically focuses on document layout analysis (DLA) and text line detection (TLD), both of which are crucial components of OCR systems. Our objective is to develop an effective method for extracting both textual and non‐textual regions, addressing challenges unique to the Persian (and Persian‐like) language(s). In the DLA stage, we employ deep learning models and a voting system to accurately determine the regions of interest. Additionally, we introduce methods such as optimum font size concepts, angle correction, and a line curvature elimination algorithm in the TLD process to enhance OCR accuracy. Comparative evaluations against state‐of‐the‐art methods demonstrate the superiority of our approach, showcasing a 2.8% improvement in the accuracy of Tesseract‐OCR 5.1.0 (a well‐established commercial OCR system) on the official Iranian newspapers dataset. These findings underscore the importance of addressing DLA and TLD challenges to advance OCR technology for Persian language documents and provide a solid foundation for future research in this domain