AWS Textract - Training

1)Optimize Image Quality: Ensure that the images of the handwritten forms you provide to Textract are of high quality. This can help Textract better recognize and interpret the handwritten text.

2. Preprocessing: Before sending the images to Textract, you can apply preprocessing techniques such as image enhancement, noise reduction, and binarization to improve the clarity of the text.

3. Custom Configuration: Textract provides some configuration options such as specifying the document language and enabling handwriting detection. Experimenting with these options may improve the accuracy for your specific use case.

4. Bounding Boxes: You can use bounding boxes to specify regions of interest within the document. This can help Textract focus on specific areas where handwritten text is present, improving overall accuracy.

5. Post-Processing: After receiving the results from Textract, you can implement post-processing techniques to further refine the extracted text. This may include applying regular expressions, context-based corrections, or language-specific rules to correct misinterpretations.

6. Training Custom Models: Although Textract doesn't currently support training custom models directly, you can preprocess your data and use other machine learning or OCR (Optical Character Recognition) techniques to train custom models tailored to your specific handwriting style or document types. Once trained, you can integrate these models with Textract or use them independently.

7)Feedback Loop:

Provide feedback to Amazon through their support channels or feedback mechanisms. This helps them improve the service over time and may lead to better accuracy for your use case in future updates.