Abstract: The present disclosure describes a method, system, and a computer readable medium for extracting tabular data present in a document. The method comprises detecting presence of at least one table in the document using a deep learning based model and a statistical method. The method further comprises identifying a type of the table based on determining a count of horizontal and vertical lines, presence of outer borders, and presence of row-column intersections in the table. The type of the table comprises a bordered table, a partially bordered table, or a borderless table. The method further comprises processing the detected table, depending on its type, to identify one or more cells present in the table. The method further comprises generating an output file by extracting the tabular data present in the table, where the extracting comprises performing optical character recognition on the identified one or more cells.

Abstract: The present disclosure describes a method, apparatus, and computer readable medium for Surface Mount Technology (SMT). The system comprising a Data Integration (DI) platform configured to collate data from one or more units in an assembly line, an Artificial Intelligence (AI) platform configured to process the collated data, using one or more machine learning techniques, to generate predictive and preventive analysis for the one or more units present in the assembly line. The system further disclose a Digital Twin Simulation (DTS) platform configured to simulate an exact replica of all the units present in the assembly line, provide visual representation, allow the one or more operators in the assembly line to take at least one action and provide the at least one action taken by the one or more operators in the assembly line as feedback signal to AI platform to improve prediction rate of said system.

Abstract: The present disclosure describes a method, apparatus, and computer readable medium for providing predictive and preventive analysis relating to fitness of elements in an assembly line, prior to final product assembling. According to the present disclosure, when the elements in assembly lines pass the testing performed by the element testing units, the test data of the elements is processed by the system to predict whether the elements when assembled, results in a fit product. When the final product is predicted to be unfit, the system detects non-reliable element(s) and provide explanation/feedback for the failure of the final product.

Abstract: The present disclosure describes a method, system, and a computer readable medium for extracting tabular data present in a document. The method comprises detecting presence of at least one table in the document using a deep learning based model and a statistical method. The method further comprises identifying a type of the table based on determining a count of horizontal and vertical lines, presence of outer borders, and presence of row-column intersections in the table. The type of the table comprises a bordered table, a partially bordered table, or a borderless table. The method further comprises processing the detected table, depending on its type, to identify one or more cells present in the table. The method further comprises generating an output file by extracting the tabular data present in the table, where the extracting comprises performing optical character recognition on the identified one or more cells.