A law firm handling civil and commercial cases maintained a large archive of printed and scanned legal documents—contracts, case records, and correspondence. Many of these documents needed to be digitized for better searchability, internal referencing, and long-term storage.
Standard digitization methods, while helpful, often produced inconsistent results, especially with older scans, annotated printouts, or documents with complex formatting.
The primary difficulties the firm encountered included:
Inaccurate Text Extraction: Traditional digitization tools frequently introduced small errors in the extracted text, such as incorrect characters or formatting, especially in documents with legal terminology and structured clauses.
Time-Consuming Corrections: These errors required manual review and correction by staff before the documents could be used confidently in day-to-day work.
Unstructured Archives: Without reliable digital text, documents couldn’t be indexed or searched effectively, making it difficult to retrieve key information quickly.
We developed a system that combined Optical Character Recognition (OCR) with an AI-based Text Correction Layer. The goal was to improve the accuracy and usefulness of digitized legal documents without increasing the review burden on staff.
Text Recognition: Each scanned document was first processed using Optical Character Recognition to extract text from the image.
Language-Based Correction: An AI model reviewed the extracted text and corrected errors based on typical patterns in legal language. This helped address common OCR issues like misread characters, broken lines, or misplaced punctuation.
Format Preservation: The system aimed to preserve the structure of the original document—such as numbered sections, indents, and signatures—so the output was both accurate and readable.
With corrected, structured text, the firm could now store documents in a format that was easily searchable and took up significantly less storage space than raw image files.
Staff could locate specific clauses, dates, or names without manually browsing through scanned pages, improving response time for internal requests and case preparation.
The digitized files were also easier to share across departments or with external partners, reducing reliance on physical storage and printouts.
By combining Optical Character Recognition (OCR) with AI-driven text correction, the firm improved the quality and usability of its digital archives.
The new process reduced manual cleanup work, preserved legal formatting, and made previously static documents searchable and lightweight. This helped the legal team access information more efficiently without disrupting their established workflows.
See it in action!
