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11 Segmentation of Handwritten Document Images into Text Lines Vassilis Katsouros and Vassilis Papavassiliou Institute for Language and Speech Processing/R.C. Athena Greece 1. Introduction There are

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How to fill out handwritten document image segmentation:

1. Obtain a scanned or photographed image of the handwritten document that you want to segment. Ensure that the image is clear and has good resolution.
2. Use image processing software or libraries to preprocess the image. This may involve operations such as cropping, resizing, grayscale conversion, and noise reduction to improve the quality of the image.
3. Apply automatic or manual segmentation techniques to separate the text regions from the background and other non-text elements. Automatic techniques may include thresholding, edge detection, or connected component analysis. Manual techniques could involve drawing regions or using selection tools in image editing software.
4. After obtaining segmented regions, you can further refine the results by removing any unwanted artifacts or noise that may have been introduced during segmentation. This can be done using morphological operations, such as erosion or dilation, or by applying algorithms tailored for text extraction.
5. Finally, save the segmented text regions as separate files or combine them into a single document. You can further process the extracted text by using Optical Character Recognition (OCR) techniques for converting it into editable formats, if desired.

Who needs handwritten document image segmentation:

1. Researchers or academics working in the field of computer vision or image processing may need handwritten document image segmentation for analyzing or studying handwriting styles, document analysis, or recognition systems.
2. Businesses or organizations that deal with large volumes of handwritten documents, such as legal firms, archives, or historical societies, may require image segmentation to automate the extraction of text and relevant information from handwritten documents.
3. Developers or engineers working on applications that involve digitizing handwritten content, such as note-taking apps, document management systems, or handwriting recognition tools, can benefit from image segmentation techniques to enhance the accuracy and usability of their solutions.

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