July 28, 2026
Most public sector IT directors we talk to are not looking to rip out their document capture platform. They inherited a system, whether it's OpenText Captiva, Datacap, Kofax, or ABBYY, sometimes a custom-built process that predates all three, and it handles the bulk of their volume without complaint. The problem isn't the platform overall. It's a specific category of documents that the platform was never designed to handle well, and that category is growing.
Understanding where these platforms actually break is the first step to deciding what to do about it.
Enterprise document capture software platforms all rely heavily on templates and zonal recognition to extract data from structured or semi structured documents. This works well when the document layout is predictable. An invoice from a known customer or vendor, a standardized intake form, a tax document with fixed field positions. The system knows where to look, and accuracy stays high.
The trouble starts when documents stop being predictable. A few situations come up repeatedly in public sector environments.
Forms submitted through online portals often arrive as scanned images of printed forms, phone photos of handwritten forms, or PDFs generated by whatever software the submitter happened to have. None of these follow the layout the template was built against. Every new submission source can mean a new template, and someone has to build and maintain each one.
Handwritten content is a separate problem entirely. Zonal OCR was built for machine print. Handwriting, especially inconsistent handwriting from the general public rather than internal staff, produces error rates that make straight through processing unrealistic. These documents usually end up in an exception queue, reviewed manually, which defeats much of the purpose of automating in the first place.
Document variability within a single form type is also common in government settings. The same application form might be submitted on letter size paper, legal size paper, scanned at an angle, or missing a page. Template based systems tend to be rigid about these variations even when a human reviewer would have no trouble reading the document.
None of this means the platform has failed. It means the platform is being asked to do something it wasn't built for, on a subset of the total volume.
When we see this pattern, the instinct from vendors is usually to propose a full platform migration. In our experience, that's rarely necessary and rarely the best use of budget. If eighty percent of a document volume is processing cleanly through the existing system, replacing that system to fix problems with the other twenty percent is expensive and disruptive for a return that could be achieved more narrowly.
A more targeted approach is to leave the existing platform in place for what it already does well, and add a thin layer, often built on AWS services, that handles the specific document types or intake sources causing trouble. The output of that layer feeds back into the existing pipeline, so downstream systems, workflows, and staff processes don't need to change.
One example from our work involved a state government client using Datacap to process incoming applications. The bulk of their volume, direct mail submissions and in office scans, worked fine within Datacap's existing template configuration. The trouble was a newer intake channel, an online portal where applicants uploaded photos or scans of forms from their phones. These images arrived skewed, poorly lit, and inconsistently cropped, and Datacap's built in recognition wasn't designed for that kind of input.
Rather than replace Datacap, we built OCR handling for that specific intake channel and integrated it directly into the existing Datacap workflow. The messy portal submissions now get processed through the added layer, and the output lands in Datacap's pipeline the same way any other document would. Staff didn't need retraining on a new platform, and the agency didn't need to justify a full system replacement to handle what was, in volume terms, an outlier case.
A separate but related scenario comes up when the core issue isn't a specific intake channel, but the document content itself, specifically handwriting or scanned images that traditional OCR struggles to read accurately regardless of which platform is doing the recognition.
In these cases, we've used vision AI and large language models to handle the extraction step directly. These models are considerably better than legacy OCR engines at reading inconsistent handwriting and interpreting scanned documents with poor image quality, because they're working from a different kind of pattern recognition than zonal template matching. The extracted data still gets passed into the organization's existing capture pipeline afterward, so the rest of the process, routing, validation, storage, downstream integration, stays the same.
The pipeline doesn't need to know or care that a different extraction method was used for that batch of documents. From the platform's perspective, structured data comes in the same way it always has.
The common thread across these scenarios is that the fix doesn't have to match the size of the platform. A legacy capture system that's underperforming on ten or twenty percent of volume doesn't require replacing the other eighty or ninety percent. It requires identifying which documents are causing the trouble, understanding why the existing recognition approach struggles with them, and deciding whether a targeted addition solves the problem without disturbing what already works.
This is usually easier to determine with a structured look at the current environment than by guessing based on general frustration with exception rates. Knowing which document types and intake sources are actually driving the manual work is what makes it possible to scope a fix that's proportional to the problem.
If you're weighing whether to patch, augment, or replace your current capture environment, it helps to have an experienced team look at the specifics with you. Our integration team is US based, and we're glad to come on site to walk through your current setup and where the gaps actually are. Reach out through our contact page or at info@daspartner.com.
.avif)
