Update README.md

README.md

@@ -8,61 +8,63 @@
 
 This model is a fine-tuned version of the microsoft/trocr-large-handwritten model, specialized for recognizing handwritten text. It has been trained on various dataset from 17th to 20th centuries and can be used for applications such as document digitization, form recognition, or any task involving handwritten text extraction.
 
-#Model Architecture
+# Model Architecture
+
 The model is based on a Transformer architecture (TrOCR) with an encoder-decoder setup:
 
 - The encoder processes images of handwritten text.
 - The decoder generates corresponding text output.
 
-#Intended Use
+# Intended Use
+
 This model is designed for handwritten text recognition and is intended for use in:
 
 - Document digitization (e.g., archival work, historical manuscripts)
 - Handwritten notes transcription
 
-#Training data
+# Training data
 The training dataset includes more than 760 000 samples of handwritten text rows, covering a wide variety of handwriting styles and text samples.
 
-#Evaluation
+# Evaluation
 The model was evaluated on test dataset. Below are key metrics:
 
 **Character Error Rate (CER):** 3.2
 **Test Dataset Description:** size ~94 900 text rows
 
-#How to Use the Model
+# How to Use the Model
 You can use the model directly with Hugging Face’s pipeline function or by manually loading the processor and model.
 
 ```from transformers import TrOCRProcessor, VisionEncoderDecoderModel
-```from PIL import Image
+from PIL import Image
 
 # Load the model and processor
-```processor = TrOCRProcessor.from_pretrained("")
-```model = VisionEncoderDecoderModel.from_pretrained("")
+processor = TrOCRProcessor.from_pretrained("")
+model = VisionEncoderDecoderModel.from_pretrained("")
 
 # Open an image of handwritten text
-```image = Image.open("path_to_image.png")
+image = Image.open("path_to_image.png")
 
 # Preprocess and predict
-```pixel_values = processor(image, return_tensors="pt").pixel_values
-```generated_ids = model.generate(pixel_values)
-```generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+pixel_values = processor(image, return_tensors="pt").pixel_values
+generated_ids = model.generate(pixel_values)
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 
-```print(generated_text)
+print(generated_text)´´´
 
-#Limitations and Biases
+# Limitations and Biases
 The model was trained primarily on handwritten text that uses basic Latin characters (A-Z, a-z) and includes Nordic special characters (å, ä, ö). It has not been trained on non-Latin alphabets, such as Chinese characters, Cyrillic script, or other writing systems like Arabic or Hebrew.
 The model may not generalize well to any other languages than Finnish, Swedish or English.
 Ethical Considerations
 Data Privacy: Be aware of any privacy concerns when applying this model to personal handwritten documents.
 Bias: Since the model is trained on specific handwriting styles and datasets, it may exhibit biases towards certain scripts or writing conventions.
 
-#Future Work
+# Future Work
 Potential improvements for this model include:
 
 - Expanding training data: Incorporating more diverse handwriting styles and languages.
 - Optimizing for specific domains: Fine-tuning the model on domain-specific handwriting.
 
-#Citation
+# Citation
 If you use this model in your work, please cite it as:
 
 @misc{multicentury_htr_model_2024,
@@ -73,7 +75,7 @@ If you use this model in your work, please cite it as:
   howpublished = {\url{https://huggingface.co/Kansallisarkisto/multicentury-htr-model/}},
 }
 
-##Model Card Authors
+## Model Card Authors
 Author: Kansallisarkisto
 Contact Information: riikka.marttila@kansallisarkisto.fi, ilkka.jokipii@kansallisarkisto.fi