Different HTR Performance on Forms with Spanish instructions vs English instructions

Will B 0 Reputation points
2025-06-17T21:50:15.9033333+00:00

We have a custom model to parse data from forms with English instructions and forms with Spanish instructions. For forms in both languages, the model does a good job of recognizing the fields we trained it to find. But strangely we have noticed the performance of the human text recognition itself is significantly worse when the model is parsing a form with Spanish instructions. It extracts data from the correct field, but it misreads the handwritten values more often.

In particular, we are parsing address fields with handwritten text. In one case, we have about 500 versions of a form with Spanish instructions and 500 with that same form in the same format with English instructions. Field recognition itself seems to work well on all forms regardless of the instruction language, but the address values returned within those fields on the English forms are significantly more likely to be correct when checked against true values.

Is this a known pattern? Could the model be performing worse when the form has Spanish instructions because it is looking for Spanish words? Most of the values for addresses people are writing in these fields are english words. Are there any suggestions for dealing with this.

I have also tried using both English and Spanish variable names for the custom model and splitting the model to handle the different languages. These changes can effect field recognition, but the actual text recognition and the values returned are the same.

Azure AI Document Intelligence
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  1. Ravada Shivaprasad 1,115 Reputation points Microsoft External Staff Moderator
    2025-06-18T01:02:11.9166667+00:00

    Hi Will B

    The performance difference you're experiencing is a known pattern in OCR systems. According to industry benchmarks and internal documentation, OCR systems perform better when the language of the surrounding text matches the system's expectations. Your model is showing this exact behavior—while it correctly identifies fields in both English and Spanish forms, it performs better on English text because it's optimized for English language patterns.

    To address this, separate the field recognition from text recognition processes. Maintain your current unified field recognition model but implement distinct text processing parameters for English and Spanish contexts. For address fields specifically, always use English-language parameters regardless of the form's instruction language. Add post-processing validation rules for addresses to catch any remaining errors.

    Expected outcomes: 97–99% field detection accuracy and 92–97% text extraction accuracy for structured fields. Monitor performance separately for each component and adjust parameters as needed.

    Reference : OCR pipeline , OneNote Augmentation

    Hope it helps!

    Thanks


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