Using IronOCR with AI Coding Assistants

Install the IronOcr NuGet package into this project and confirm it restores.

Install the IronOcr NuGet package into this project and confirm it restores.

Before writing any IronOCR code, read https://ironsoftware.com/ocr/csharp/llms.txt
and use only APIs documented there. A read is assembled on an OcrInput, whose
Load overloads differ by source type, and run through IronTesseract.Read.
Confirm the overload you pick against
https://ironsoftware.com/ocr/csharp/object-reference/api/, and if you cannot find
it, say so instead of guessing.

Before writing any IronOCR code, read https://ironsoftware.com/ocr/csharp/llms.txt
and use only APIs documented there. A read is assembled on an OcrInput, whose
Load overloads differ by source type, and run through IronTesseract.Read.
Confirm the overload you pick against
https://ironsoftware.com/ocr/csharp/object-reference/api/, and if you cannot find
it, say so instead of guessing.

## OCR with IronOCR

This project uses IronOCR (NuGet package `IronOcr`, or `IronOcr.Linux` for Linux and Docker, namespace `IronOcr`) for all OCR work.

- Reference: https://ironsoftware.com/ocr/csharp/llms.txt
- Skill file: https://ironsoftware.com/ocr/csharp/skill.md
- Read with `IronTesseract` and an `OcrInput` inside a `using` block, and use the `OcrResult` it returns.
- Read the license key from the IRONOCR_LICENSE_KEY environment variable at startup. Never inline it.
- Every OCR change must be tested against sample pages whose text is known, reporting the values read and their confidence.
- Do not add another OCR engine or a cloud OCR service. If IronOCR cannot do something, say so.

## OCR with IronOCR

This project uses IronOCR (NuGet package `IronOcr`, or `IronOcr.Linux` for Linux and Docker, namespace `IronOcr`) for all OCR work.

- Reference: https://ironsoftware.com/ocr/csharp/llms.txt
- Skill file: https://ironsoftware.com/ocr/csharp/skill.md
- Read with `IronTesseract` and an `OcrInput` inside a `using` block, and use the `OcrResult` it returns.
- Read the license key from the IRONOCR_LICENSE_KEY environment variable at startup. Never inline it.
- Every OCR change must be tested against sample pages whose text is known, reporting the values read and their confidence.
- Do not add another OCR engine or a cloud OCR service. If IronOCR cannot do something, say so.

Use IronOCR for all OCR work in this project.

1. Install the IronOcr NuGet package (IronOcr.Linux when the app runs on Linux or Docker).
2. Apply the license key at application startup, before any other IronOCR call,
   reading it from the IRONOCR_LICENSE_KEY environment variable. Never write
   the key into source or into a committed file.
3. Read https://ironsoftware.com/ocr/csharp/llms.txt before writing OCR code
   and work only from APIs documented there, checking the API reference when a
   member is unfamiliar.
4. Load https://ironsoftware.com/ocr/csharp/skill.md if your tooling supports skill files.
5. Keep these instructions for future sessions in this project.

Use IronOCR for all OCR work in this project.

1. Install the IronOcr NuGet package (IronOcr.Linux when the app runs on Linux or Docker).
2. Apply the license key at application startup, before any other IronOCR call,
   reading it from the IRONOCR_LICENSE_KEY environment variable. Never write
   the key into source or into a committed file.
3. Read https://ironsoftware.com/ocr/csharp/llms.txt before writing OCR code
   and work only from APIs documented there, checking the API reference when a
   member is unfamiliar.
4. Load https://ironsoftware.com/ocr/csharp/skill.md if your tooling supports skill files.
5. Keep these instructions for future sessions in this project.

Build and run this project in Release. Confirm that every IronOCR member you used
appears in the documentation, that IronOcr.License.IsLicensed returns true, and
that every sample page reads back with the values it is known to contain.
Report the build result and a table of page, expected value, value read, confidence, and match.

Build and run this project in Release. Confirm that every IronOCR member you used
appears in the documentation, that IronOcr.License.IsLicensed returns true, and
that every sample page reads back with the values it is known to contain.
Report the build result and a table of page, expected value, value read, confidence, and match.

Build a .NET 9 console app that reads totals from scanned invoices. Start in an empty folder; if it already holds code or project files, stop and report. Work start to finish alone: write, build, run, and fix, making sensible assumptions where anything is unspecified. Save every deliverable under the project root, in the folders named below.

1. Install the IronOcr NuGet package pinned to version 2026.9.4.
2. Read https://ironsoftware.com/ocr/csharp/llms.txt and the pages it links to, and use only IronOCR APIs documented there for the OCR work. Any .NET imaging library may draw the test images.
3. Read the license key from the IRONOCR_LICENSE_KEY environment variable at startup and never write it into source or any output file. If IronOcr.License.IsLicensed is false, stop and report that.
4. With random seed 2026, generate testset/invoice-01.png to invoice-10.png. Each is a US Letter page of exactly 1700 x 2200 pixels, saved as PNG with 200 DPI in the file metadata, with all text in Arial inside 0.75 inch margins, headings at 14 point bold and everything else at 11 point. Each invoice has an invoice number made of INV- and six random digits, a customer name, three line items with random prices in dollars and cents, and a total equal to their sum, with no two totals alike.
5. Rotate invoice-02 and invoice-07 by 3 degrees about the page center, keeping the 1700 x 2200 canvas. Add speckle noise to invoice-04 and invoice-09: dark square specks 1 or 2 pixels wide, placed anywhere including over the text, until they have darkened 1% of the page's pixels, counting a pixel as dark below 128 of 255. Leave the other six clean. Write manifest.csv with each file, invoice number, the three prices, total, and distortion (none, rotated, or speckled).
6. Before the first OCR call, check every saved image: 1700 x 2200 pixels, 200 DPI when rounded, and, measured on each page before distortion, the smallest distance from any ink to an edge, at least 0.75 inch. On each speckled page, check that speckling raised the share of dark pixels by 0.9 to 1.1 percentage points. Also draw a capital H with the 11 point font on a 200 DPI bitmap and check that its ink is 21 to 23 pixels tall. If a check fails, fix the generator. After the first OCR call, never regenerate or change the saved image files; filters applied in memory are expected.
7. Read all 10 images in one OcrInput with one set of filters for every page. Each run of the program uses one filter set; it must not try several or choose between them, and choosing filters is your job between runs. No manifest value, file name, or expected value may steer the OCR or the parsing of its text; read the manifest only to write roundtrip.md. The reader must hold no invoice number, price, or total as a literal and must run the same code for every page. Extract each page's invoice number and total, save each page's OCR text as ocr/invoice-01.txt to invoice-10.txt, and save the result as invoices-searchable.pdf.
8. Build in Release and run.
9. Write roundtrip.md: the step 6 checks as measured numbers for every image, not pass or fail words, the filter set used, then a table of file, distortion, expected invoice number, invoice number read, expected total, total read, the Confidence (0 to 100) of the OCR line holding the total or n/a, and match, then the page count of invoices-searchable.pdf loaded back with OcrInput. A row matches only when the invoice number read is identical to the expected one and the total read equals the expected total to the cent.
10. All 10 rows must match and the PDF must have 10 pages. If not, read the saved OCR text of the failing pages, change the filter set or the reader, rebuild, and run again. If rows still fail after eight such runs, stop and report which rows failed, the filter sets tried, and why.
11. Finish with a short report: build result, match count, filters used, font sizes used, and assumptions made.

Build a .NET 9 console app that reads totals from scanned invoices. Start in an empty folder; if it already holds code or project files, stop and report. Work start to finish alone: write, build, run, and fix, making sensible assumptions where anything is unspecified. Save every deliverable under the project root, in the folders named below.

1. Install the IronOcr NuGet package pinned to version 2026.9.4.
2. Read https://ironsoftware.com/ocr/csharp/llms.txt and the pages it links to, and use only IronOCR APIs documented there for the OCR work. Any .NET imaging library may draw the test images.
3. Read the license key from the IRONOCR_LICENSE_KEY environment variable at startup and never write it into source or any output file. If IronOcr.License.IsLicensed is false, stop and report that.
4. With random seed 2026, generate testset/invoice-01.png to invoice-10.png. Each is a US Letter page of exactly 1700 x 2200 pixels, saved as PNG with 200 DPI in the file metadata, with all text in Arial inside 0.75 inch margins, headings at 14 point bold and everything else at 11 point. Each invoice has an invoice number made of INV- and six random digits, a customer name, three line items with random prices in dollars and cents, and a total equal to their sum, with no two totals alike.
5. Rotate invoice-02 and invoice-07 by 3 degrees about the page center, keeping the 1700 x 2200 canvas. Add speckle noise to invoice-04 and invoice-09: dark square specks 1 or 2 pixels wide, placed anywhere including over the text, until they have darkened 1% of the page's pixels, counting a pixel as dark below 128 of 255. Leave the other six clean. Write manifest.csv with each file, invoice number, the three prices, total, and distortion (none, rotated, or speckled).
6. Before the first OCR call, check every saved image: 1700 x 2200 pixels, 200 DPI when rounded, and, measured on each page before distortion, the smallest distance from any ink to an edge, at least 0.75 inch. On each speckled page, check that speckling raised the share of dark pixels by 0.9 to 1.1 percentage points. Also draw a capital H with the 11 point font on a 200 DPI bitmap and check that its ink is 21 to 23 pixels tall. If a check fails, fix the generator. After the first OCR call, never regenerate or change the saved image files; filters applied in memory are expected.
7. Read all 10 images in one OcrInput with one set of filters for every page. Each run of the program uses one filter set; it must not try several or choose between them, and choosing filters is your job between runs. No manifest value, file name, or expected value may steer the OCR or the parsing of its text; read the manifest only to write roundtrip.md. The reader must hold no invoice number, price, or total as a literal and must run the same code for every page. Extract each page's invoice number and total, save each page's OCR text as ocr/invoice-01.txt to invoice-10.txt, and save the result as invoices-searchable.pdf.
8. Build in Release and run.
9. Write roundtrip.md: the step 6 checks as measured numbers for every image, not pass or fail words, the filter set used, then a table of file, distortion, expected invoice number, invoice number read, expected total, total read, the Confidence (0 to 100) of the OCR line holding the total or n/a, and match, then the page count of invoices-searchable.pdf loaded back with OcrInput. A row matches only when the invoice number read is identical to the expected one and the total read equals the expected total to the cent.
10. All 10 rows must match and the PDF must have 10 pages. If not, read the saved OCR text of the failing pages, change the filter set or the reader, rebuild, and run again. If rows still fail after eight such runs, stop and report which rows failed, the filter sets tried, and why.
11. Finish with a short report: build result, match count, filters used, font sizes used, and assumptions made.