A processor implemented method, comprising: receiving, via one or more hardware processors, a scanned image document comprising one or more tables and text; extracting and highlighting, via the one or more hardware processors, at least one of one or more non-numerical values and one or more numerical values from the one or more tables and associated text, using a text recognition technique based on identified semantic information using the text from the scanned image document; inputting, via the one or more hardware processors, the extracted and highlighted non-numerical and numerical values being appended to the scanned image document, to a deep learning network to obtain a set of learnt features, wherein training the deep learning network comprises: providing basic semantic type information of the scanned image to the deep learning network by: processing the scanned image by using optical character recognition technique to get word patches in the image document; processing the word patches via regular expressions to determine their data-type; giving each datatype a unique color and shading bounding-boxes of words with similar datatypes in the same color; filtering the word bounding boxes to remove spurious detections; dropping randomly selected word patches to simulate the case of incomplete word detection; pixel-wise adding the word shaded image to the scanned image to obtain modified image used for training; generating, via the one or more hardware processors, a masked table region and one or more masked column regions using the extracted and highlighted non-numerical and numerical values based on the set of learnt features to determine boundary of the one or more columns and the one or more tables comprised in the scanned image document to obtain: (i) 3 boxes representing columns, and (ii) 1 large box representing table; applying, via the one or more hardware processors, one or more domain-based rules on the masked table region to obtain one or more rows; and identifying, via the one or more hardware processors, tuples in the one or more tables based on one or more columns from the masked column regions and the one or more obtained rows from the masked table region. receiving, via one or more hardware processors, a scanned image document comprising one or more tables and text; extracting and highlighting, via the one or more hardware processors, at least one of one or more non-numerical values and one or more numerical values from the one or more tables and associated text, using a text recognition technique based on identified semantic information using the text from the scanned image document; inputting, via the one or more hardware processors, the extracted and highlighted non-numerical and numerical values being appended to the scanned image document, to a deep learning network to obtain a set of learnt features, wherein training the deep learning network comprises: providing basic semantic type information of the scanned image to the deep learning network by: processing the scanned image by using optical character recognition technique to get word patches in the image document; processing the word patches via regular expressions to determine their data-type; giving each datatype a unique color and shading bounding-boxes of words with similar datatypes in the same color; filtering the word bounding boxes to remove spurious detections; dropping randomly selected word patches to simulate the case of incomplete word detection; pixel-wise adding the word shaded image to the scanned image to obtain modified image used for training; processing the scanned image by using optical character recognition technique to get word patches in the image document; processing the word patches via regular expressions to determine their data-type; giving each datatype a unique color and shading bounding-boxes of words with similar datatypes in the same color; filtering the word bounding boxes to remove spurious detections; dropping randomly selected word patches to simulate the case of incomplete word detection; pixel-wise adding the word shaded image to the scanned image to obtain modified image used for training; generating, via the one or more hardware processors, a masked table region and one or more masked column regions using the extracted and highlighted non-numerical and numerical values based on the set of learnt features to determine boundary of the one or more columns and the one or more tables comprised in the scanned image document to obtain: (i) 3 boxes representing columns, and (ii) 1 large box representing table; applying, via the one or more hardware processors, one or more domain-based rules on the masked table region to obtain one or more rows; and identifying, via the one or more hardware processors, tuples in the one or more tables based on one or more columns from the masked column regions and the one or more obtained rows from the masked table region.