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Text document photos remain a highly important resource in the development or training of

document segmentation and Optical Character Recognition (OCR) applications. This is due to the

quality assessment of algorithms for these tasks is performed using unknown images, which

guarantees their robustness, and that Machine Learning techniques need large datasets with a great

number of samples with high inter and intraclass diversity. This work presents a method to

procedurally generate simulations of photos of text documents with different backgrounds. Using

Digital Image Processing techniques, this process eliminates the need of creating a real dataset by

hand and increases the variability of the available data. The dataset generated can have any number

of samples, can be used in models that need smaller or larger datasets.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.