A company dealing with huge volumes of scanned and digital documents needed to identify and tag words, phrases and relationships between documents. But how could they decide on the proposed solution?
Benchmarking - which OCR among the market leaders is most effective across a wide variety of document qualities?
Validation - how is it possible to measure the AI effectiveness?
Optimisation - how can document features be identified that impact the OCR performance?
Advai's system identifies that the OCR AI is likely to struggle and mis-classify when certain conditions in images occur. This issue is solved in advance, resulting in greater accuracy in those documents.
Recommendations for best OCR systems were implemented based on Advai performance statistics.
Key features that cause interference are captured and mapped for each input document.
By understanding the strengths and weaknesses of the OCR AIs, the customer is able to pick the best for their situation.
The data scientist is able to proactively identify issues before deployment. No more trying to hypothesise where the issues are going to be or trying to solve them through simply collecting more data!
Because the customer knows which parts of their AI models perform well or poorly, they are to triage problem areas and prioritise what to focus on , saving valuable man hours and data collection costs.