Abstract: A method of document image processing comprises, based on at least a document page image, generating a plurality of semantic tokens that includes a plurality of word tokens and a plurality of special tokens. Each special token among the plurality of special tokens represents a non-textual semantic element of the document image, and generating the plurality of semantic tokens includes predicting, for each special token among the plurality of special tokens, a token type of the special token. The method also comprises generating, for each semantic token among the plurality of semantic tokens, a corresponding semantic token embedding among a plurality of semantic token embeddings; and applying a trained model to process an input that is based on the plurality of semantic token embeddings and a plurality of visual token embeddings based on at least the document page image to generate a semantic processing result.

Abstract: In some embodiments, techniques for knowledge-based validation of entities extracted from digital documents are provided. For example, a process may involve selecting, from among a plurality of entities extracted from a digital document, a first set of correlated entities. Selecting the first set of correlated entities may be based on a correlation that is indicated by relative location of the entities of the first set of correlated entities within the digital document or by similarity among tags of the entities of the first set. The method may also include determining, using a knowledge model, that the first set of correlated entities is not valid; and generating, using the knowledge model, a first modified set of correlated entities, wherein each entity of the first modified set of correlated entities corresponds to a respective entity of the first set of correlated entities.

Abstract: In some embodiments, techniques for document entity extraction are provided. For example, a process may involve processing document images to detect a plurality of regions of interest that includes text objects and non-text objects; for each of the plurality of regions of interest, producing a corresponding text string; and processing the text strings to identify entities. Processing the document images may involve applying a text object detection model to the document images to detect the text objects; and applying at least one non-text object detection model to the document images to detect the non-text objects. Prior to processing the document images, at least two object detection models among the text object detection model and the at least one non-text object detection model were generated by fine-tuning respective instances of a pre-trained object detection model.

Abstract: A computer-implemented method includes accessing, by a processor, an unstructured document. The method also includes performing, by the processor, box detection on the unstructured document to generate a box graph of the unstructured document. Further, the method includes integrating, by the processor, identified text of the unstructured document with the box graph of the unstructured document to build a text graph. Furthermore, the method includes generating, by the processor, a structured text representation of the unstructured document using the text graph.

Abstract: A computer-implemented method, includes accessing, by a processor, a set of asset documents. The method also includes performing, by the processor, feature extraction on text of each document of the set of asset documents using a feature extraction module to generate a set of features, where each feature of the set of features represents a document of the set of asset documents. The method also includes generating, by the processor, a set of lower-dimensional features from the set of features using a singular value decomposition module. The method also includes generating, by the processor, a set of clusters from the set of lower-dimensional features using a clustering module. The method also includes training, by the processor, a machine-learning model of a classification microservice using the set of clusters generated from the clustering module.

Abstract: A flow synthesis system (FSS) based on contamination free PTFE tubing, a pump for pumping requisite solutions and a heater for heating precipitated flow suspensions has been designed. Synthesis, using FSS, eliminates the need for secondary heat-treatments and/or long ageing times required in traditional inorganic synthesis routes. The FSS was used successfully to synthesis calcium phosphates which include phase-pure and ion substituted hydroxyapatite, respectively. Biologically beneficial magnesium, zinc, carbonate and silicon ions were successfully incorporated into hydroxyapatite.

Abstract: A method for synthesis of inorganic nanoparticles is disclosed. This synthesis method is single step, based on two heaters and functions using near ambient conditions. This flow method can be used to synthesize a range of inorganic particles. Synthesis of stoichiometric and non-stoichiometric hydroxyapatite with ranging thermal stabilities has been shown in this application. These materials find wide applications as biomaterials, in the form of additives to polymer based composites, for bone filling applications and also as coatings on metallic substrates.