Update README.md

README.md

@@ -57,16 +57,19 @@ HTR for the same tokens with page to page congruence, and broadly line by line c
    * Process Step Two: Run LLM-based process to (a) auto-correct clear errors (b) escalating 
      correction options to a human expert, providing logic, and requesting a decision
      
-5. Examine existing benchmarks for transcription accuracy and apply to fine-tuned models
+5. Examine existing benchmarks for transcription accuracy and apply to fine-tuned models and develop domain specific
+   benchmarkes for transcription accuracy and apply to fine-tuned models
 
-6. Develop domain specific benchmarkes for transcription accuracy and apply to fine-tuned models
-
-7. User testing of output of fine-tuned small LLMs
+7. User testing of impact of corrections via fine-tuned small LLMs
    * Correction of single letter errors in word
    * Correction of doule letter errors in word
    * Correction of single letter omission in word
    * Correction of double letter ommission in word
-
+     
+8. User testing of readability of raw HTR and different levels of machine and hand correction
+   * Impact on readability of raw HRT + rules based Python script optimised to domain
+   * Impact on readability of raw HTR + rules based Python script optimised to domain + different categories of fin-tuned SMALL LLM machine adjustment
+   
 DATASETS
 
 We have three datasets available to researchers working on Early Modern English in the late C16th and