What is Document Intelligence?

Today, industries need more and more documents, and many organizations or transactions rely on paper documents such as invoices, contracts, legal regulations, and financial statements. Converting paper documents into electronic documents has greatly improved the organization problem. Accurate extraction and intelligent use of these electronic documents will play a big role. Artificial intelligence and machine learning play a major role and value in this area, and the application of OCR recognition and NLP for text processing has greatly improved the accuracy of automated document processing.

Nexdata's intelligent document solutions provide customers with a personalized experience for everything. The most complex and diverse documents are uniquely processed accurately. Our data solutions for intelligent documents have been successfully applied in a variety of industry scenarios such as finance, insurance, retail, logistics, healthcare, and government.

For example, our work with an industry-leading office software company helped collect and label tens of thousands of invoices. The entire project consistently met their needs for all phases of software development, maintaining an acceptance rate of up to 99% accuracy, far exceeding the company's expectations. As a result, the client was able to successfully develop a smart office product that satisfied its users.

With a team of experienced linguists and a wealth of project experience, Nexdata is your trusted partner for intelligent document data.

100 People - Handwriting OCR Data of Japanese and Korean 

This dadaset was collected from 100 subjects including 50 Japanese, 49 Koreans and 1 Afghan. For different subjects, the corpus are different. The data diversity includes multiple cellphone models and different corpus. This dataset can be used for tasks, such as handwriting OCR data of Japanese and Korean.

71,535 Images English OCR Data in Natural Scenes

The collecting scenes of this dataset are the real scenes in Britain and the United States. The data diversity includes multiple scenes, multiple photographic angles and multiple light conditions. For annotation, line-level & word-leve & character-level rectangular bounding box or quadrilateral bounding box annotation were adopted, the text transcription was also adopted. The dataset can be used for English OCR tasks in natural scenes.