OCR using Vision doesn't complete document - gets most of document but fails to read parts at the bottom and right of image

Hi Timothy E Hu

When processing JPG files using Optical Character Recognition (OCR), it is not uncommon to encounter incomplete document recognition—particularly in the bottom and bottom-right areas of the image. This issue typically arises due to resource limitations and architectural constraints in traditional OCR systems. These systems often process the entire image in a single pass, and when dealing with large or high-resolution JPG files, they may exceed memory or processing thresholds. This can cause the OCR engine to terminate prematurely, leaving portions of the document—especially the lower sections—unprocessed.

The root cause lies in how traditional OCR engines allocate memory and handle image data. Without dynamic tiling or segmentation strategies, these engines struggle to maintain consistent recognition across the entire document, especially when the image contains dense text or complex layouts. This limitation becomes more pronounced with larger documents or those containing extensive data.

To address this challenge, several practical solutions can be implemented. One effective approach is to split large images into smaller, manageable sections, process each independently, and then merge the results. This reduces the memory load and improves recognition accuracy. Additionally, optimizing image parameters—such as reducing resolution (while maintaining legibility), converting to grayscale, or applying lossless compression—can enhance processing efficiency.

In more complex scenarios, implementing Intelligent Document Processing (IDP) solutions can provide even greater accuracy and scalability. These systems combine OCR with AI and machine learning to adaptively process documents, monitor resource usage, and ensure complete extraction.

Finally, it is essential to implement quality control measures such as monitoring OCR engine resource utilization, validating extracted text against the original image, and logging incomplete outputs for further analysis. These steps help ensure that the entire document is processed accurately and that any issues are promptly identified and resolved.

Reference : Azure Document Intelligence documentation , OCR - Optical Character Recognition

Hope it helps!

Thank you