A system for facilitating an automated detection of an object in a test document (502, 902), wherein the test document is a scanned document, the system comprising: a processor (102, 605) coupled to a data lake, wherein the data lake is configured to: store digitized input datasets comprising a plurality of input images (202, 802) and a plurality of original documents (204, 804), wherein each input image includes a pre-defined representation of the object; and the processor comprising a dataset generator to: obtain, from the data lake, a first input image (304, 404) from the plurality of input images and a first original document from the plurality of original documents, the first input image bearing a first pre-defined representation of the object; prune (704, 816) a portion of the first original document to obtain a pruned image; and blend, using a blending technique, the first input image with the pruned image to obtain a modified image, wherein the blending technique comprises a wavelet transformation technique and the modified image includes the pruned image bearing the first pre-defined representation; wherein the modified image is combined (708, 826) with the first original document to generate a training dataset, the training dataset being utilized to train a neural network based model to obtain a trained model, and wherein the trained model is used for the automated detection of the object in the test document. a processor (102, 605) coupled to a data lake, wherein the data lake is configured to: store digitized input datasets comprising a plurality of input images (202, 802) and a plurality of original documents (204, 804), wherein each input image includes a pre-defined representation of the object; and the processor comprising a dataset generator to: obtain, from the data lake, a first input image (304, 404) from the plurality of input images and a first original document from the plurality of original documents, the first input image bearing a first pre-defined representation of the object; prune (704, 816) a portion of the first original document to obtain a pruned image; and blend, using a blending technique, the first input image with the pruned image to obtain a modified image, wherein the blending technique comprises a wavelet transformation technique and the modified image includes the pruned image bearing the first pre-defined representation; wherein the modified image is combined (708, 826) with the first original document to generate a training dataset, the training dataset being utilized to train a neural network based model to obtain a trained model, and wherein the trained model is used for the automated detection of the object in the test document. obtain, from the data lake, a first input image (304, 404) from the plurality of input images and a first original document from the plurality of original documents, the first input image bearing a first pre-defined representation of the object; prune (704, 816) a portion of the first original document to obtain a pruned image; and blend, using a blending technique, the first input image with the pruned image to obtain a modified image, wherein the blending technique comprises a wavelet transformation technique and the modified image includes the pruned image bearing the first pre-defined representation; wherein the modified image is combined (708, 826) with the first original document to generate a training dataset, the training dataset being utilized to train a neural network based model to obtain a trained model, and wherein the trained model is used for the automated detection of the object in the test document.