Project Overview


The customer needed a powerful tool capable of parsing various structured entities from unstructured text recognized through OCR (Optical Character Recognition). The challenge was to extract critical information efficiently and accurately from text, enhancing data usability and facilitating decision-making.


DataObrii developed a comprehensive Named Entity Recognition (NER) that included various analyzers designed to parse different types of entities from unstructured text.

Key components of the solution included:

-  entity names parser;
-  address parser;
-  date parser;
-  quantities and measures parser;
-  money parser;
-  the efficient mechanism to minimize false detections;
-  the mechanism for automated labelling of the training dataset.


The implementation of the NLP-based Named Entity Recognition had a substantial impact on our client's data processing capabilities. By automating the extraction of various structured entities, it optimized the analysis of unstructured text data. This resulted in improved data accuracy, reduced manual effort, and faster decision-making processes.
DataObrii's commitment to delivering advanced solutions that address complex data challenges is evident in this project. We combine cutting-edge technology and expertise to create solutions that empower our customers to unlock the true potential of their data, enhance operational efficiency, and make informed decisions with confidence.


Python 2.7, Tensorflow, Stanford core NLP, Spacy, Grobid, datefinder, nltk, quantulum.


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