Handwritten Document Analysis, Recognition and Interpretation

Our technology encompasses a range of methodologies dedicated to the analysis, recognition, and interpretation of handwritten documents, spanning textual manuscripts, ciphers featuring rare scripts, and music scores.

 

Recognizing handwritten documents presents significant challenges due to the wide variability in handwriting styles, diverse layouts, languages, and domains. Moreover, the lack of enough annotated data poses a major obstacle, particularly in the realm of Deep Learning.

 

To address these challenges, we have developed a multifaceted approach that integrates several innovative strategies. First, few-shot learning enables us to learn effectively with limited data, making it particularly useful for recognizing manuscripts with rare scripts, unfamiliar alphabets, or distinct handwriting styles. Second, self-supervised learning can learn representations from unlabeled data, which can be effectively transferred to document recognition tasks. Third, synthetic document generation provides an alternative approach by generating labeled data to train deep learning models.