OCR that checks its own answers, as an app or an API
space ocr turns photos of receipts, invoices and forms into a table you can query. Drop them into a folder in the app or send them to the API, and each page becomes a row you can filter and sort. Every value shows where it came from. 100 free pages a month.
I made space ocr because I kept not trusting OCR output.
Reading a document is the easy part now. Knowing whether the number you got back is the number actually printed on the paper is not. If you still have to open the image and check by hand, you haven't really automated anything.
There are two ways in, and both run the same pipeline.
If you don't want to write code, you upload photos into a folder and they become a sheet. Hover any cell and the photo beside it lights up on the exact spot that value was read from, zoomed in, so checking a page takes a second instead of a squint. Cells that failed the check are marked, so you know which ones to look at rather than rereading all of them. Fix a value by hand and your correction sticks. Folders, memos and search across everything you have scanned are in there too.
If you do write code, three endpoints give you structured fields, markdown, or plain text, and all of them come back with the same verification data: where each value sits on the page, whether it passed the check, and what still needs a look.
Either way the results stay somewhere you can use, so there is no database to stand up. A folder and a sheet are the storage. Photos land in the sheet as rows of the columns you asked for, and later you can ask that sheet for the rows over an amount, or from one vendor, newest first, a page at a time. That runs on the server, it does not read the images again, and it is not charged. The rows keep the coordinates and the flags they were stored with, so a filtered answer is as checkable as a single scan.
If you would rather have an agent do the filing, there is a hosted MCP server on the same account. You point an MCP client at one URL with your key and it can make the folders and sheets, upload photos into them, and ask for rows later. Deleting is the one thing it cannot do in one step. The first call removes nothing and reports what would go, so it has to come back to you before anything disappears.
The checking itself is the part I care about. The model never produces coordinates. Every value it returns is matched character by character against what the OCR engine actually saw on the page. Values that fail get flagged instead of quietly passing, and the ones it still isn't sure about are cropped out of the image and read a second time.
I measured this on my own regression corpus, 333 hand graded cells from phone photos rather than flat scans. Turning the checking stages off drops accuracy from 93.7% to 91.3%. They fixed 22 cells and broke none. A value marked unverified turns out to be wrong 6.4 times more often than average, so the flag is worth acting on.
100 pages a month are free and failed scans are never billed. Same price whichever way you use it.
What I would really like to hear: what would make you trust OCR output enough to skip the manual check? That is the part I keep getting wrong.
Yongha
About space ocr on Product Hunt
“OCR that checks its own answers, as an app or an API”
space ocr launched on Product Hunt on August 4th, 2026 and earned 104 upvotes and 11 comments, placing #14 on the daily leaderboard. space ocr turns photos of receipts, invoices and forms into a table you can query. Drop them into a folder in the app or send them to the API, and each page becomes a row you can filter and sort. Every value shows where it came from. 100 free pages a month.
On the analytics side, space ocr competes within Productivity, API and Developer Tools — topics that collectively have 1.3M followers on Product Hunt. The dashboard above tracks how space ocr performed against the three products that launched closest to it on the same day.
Who hunted space ocr?
space ocr was hunted by Yongha Hwang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Yongha Hwang
Hi Product Hunt,
I made space ocr because I kept not trusting OCR output.
Reading a document is the easy part now. Knowing whether the number you got back is the number actually printed on the paper is not. If you still have to open the image and check by hand, you haven't really automated anything.
There are two ways in, and both run the same pipeline.
If you don't want to write code, you upload photos into a folder and they become a sheet. Hover any cell and the photo beside it lights up on the exact spot that value was read from, zoomed in, so checking a page takes a second instead of a squint. Cells that failed the check are marked, so you know which ones to look at rather than rereading all of them. Fix a value by hand and your correction sticks. Folders, memos and search across everything you have scanned are in there too.
If you do write code, three endpoints give you structured fields, markdown, or plain text, and all of them come back with the same verification data: where each value sits on the page, whether it passed the check, and what still needs a look.
Either way the results stay somewhere you can use, so there is no database to stand up. A folder and a sheet are the storage. Photos land in the sheet as rows of the columns you asked for, and later you can ask that sheet for the rows over an amount, or from one vendor, newest first, a page at a time. That runs on the server, it does not read the images again, and it is not charged. The rows keep the coordinates and the flags they were stored with, so a filtered answer is as checkable as a single scan.
If you would rather have an agent do the filing, there is a hosted MCP server on the same account. You point an MCP client at one URL with your key and it can make the folders and sheets, upload photos into them, and ask for rows later. Deleting is the one thing it cannot do in one step. The first call removes nothing and reports what would go, so it has to come back to you before anything disappears.
The checking itself is the part I care about. The model never produces coordinates. Every value it returns is matched character by character against what the OCR engine actually saw on the page. Values that fail get flagged instead of quietly passing, and the ones it still isn't sure about are cropped out of the image and read a second time.
I measured this on my own regression corpus, 333 hand graded cells from phone photos rather than flat scans. Turning the checking stages off drops accuracy from 93.7% to 91.3%. They fixed 22 cells and broke none. A value marked unverified turns out to be wrong 6.4 times more often than average, so the flag is worth acting on.
100 pages a month are free and failed scans are never billed. Same price whichever way you use it.
What I would really like to hear: what would make you trust OCR output enough to skip the manual check? That is the part I keep getting wrong.
Yongha