Automatic Table Detection, Structure Recognition and Data Extraction from Document Images
Borra Vineetha1, D. N. D. Harini2, Ravi Yelesvarupu3
1Borra Vineetha*, Department of Computer Science and Engineering, GVP College of Engineering, Visakhapatnam (A.P.), India.
2D. N. D. Harini, Department of Computer Science and Engineering, GVP College of Engineering, Visakhapatnam (A.P.), India
3Ravi Yelesvarupu, CEO, Hallmark Solutions, Visakhapatnam (A.P.), India.
Manuscript received on June 20, 2021. | Revised Manuscript received on June 30, 2021. | Manuscript published on July 30, 2021. | PP: 73-79 | Volume-10, Issue-9, July 2021 | Retrieval Number: 100.1/ijitee.I93490710921 | DOI: 10.35940/ijitee.I9349.0710921
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the recent advancement, the extensive usage of electronic devices to photograph and upload documents, the requirement for extracting the information present in the unstructured document images is becoming progressively intense. The major obstacle to the objective is, these images often contain information in tabular form and extracting the data from table images presents a series of challenges due to the various layouts and encodings of the tables. It includes the accurate detection of the table present in an image and eventually recognizing the internal structure of the table and extracting the information from it. Although some progress has been made in table detection, obtaining the table contents is still a challenge since this involves more fine-grained table structure (rows and columns) recognition. The digitization of critical information has to be carried out automatically since there are millions of documents. Based on the motivation that AI-based solutions are automating many processors, this work comprises three different stages: First, the table detection using Faster R-CNN algorithm. Second, table internal structure recognition process using morphology operation and refine operation and last the table data extraction using contours algorithm. The dataset used in this work was taken from the UNLV dataset
Keywords: Deep Learning, OCR, Scanned documents, Table detection, Structure recognition, Table data extraction