updated model to extract bank_name and cheque_date

app.py

@@ -12,18 +12,21 @@ demo = gr.Blocks()
 with demo:
     
     gr.Markdown("# **<p align='center'>ChequeEasy: Banking with Transformers </p>**")
-    gr.Markdown("ChequeEasy is a project that aims to simplify the process of approval of cheques. Leveraging recent advances in Visual Document Understanding (VDU) domain to extract relevant data from cheques and make the whole process quicker and easier for both bank officials and customers. \
-    This project leverages Donut model proposed in this <a href=\"https://arxiv.org/abs/2111.15664/\">paper </a> for the parsing of the required data from cheques." \
-    "Donut is based on a very simple transformer encoder and decoder architecture. It's main USP is that it is an OCR-free approach to information extraction from documents. \
-    OCR based techniques come with several limitations such as use of additional downstream models, lack of understanding about document structure, use of hand crafted rules,etc. \
-    Donut helps you get rid of all of these OCR specific limitations. The model for the project has been trained using this <a href=\"https://huggingface.co/datasets/shivi/cheques_sample_data/\"> dataset </a>. This HF dataset is actually a filtered version of this  <a href=\"https://www.kaggle.com/datasets/medali1992/cheque-images/\"> kaggle dataset </a>.")
+    gr.Markdown("ChequeEasy is a project that aims to simplify the process of approval of cheques and making it easier for both bank officials and customers. \
+    This project leverages Donut model proposed in the paper <a href=\"https://arxiv.org/abs/2111.15664/\"> OCR-free Document Understanding Transformer </a> for the parsing of the required data from cheques." \
+    "Donut is based on a very simple transformer encoder and decoder architecture. It's main USP is that it is an OCR-free approach to Visual Document Understanding (VDU) and can perform tasks like document classification, information extraction as well as VQA. \
+    OCR based techniques come with several limitations such as requiring use of additional downstream models, lack of understanding about document structure, requiring use of hand crafted rules for information extraction,etc. \
+    Donut helps you get rid of all of these OCR specific limitations. The model for the project has been trained using a subset of this  <a href=\"https://www.kaggle.com/datasets/medali1992/cheque-images/\"> kaggle dataset </a>. The original dataset contains images of cheques of 10 different banks. \
+    A filtered version of this dataset containing images of cheques from 4 banks that are more commonly found in the Indian Banking Sector was created with ground truth prepared in the format required for fine-tuning Donut. This <a href=\"https://huggingface.co/datasets/shivi/cheques_sample_data/\"> dataset </a> is available on the Hugging Face Hub for download.")
 
     
     with gr.Tabs():
         
         with gr.TabItem("Cheque Parser"):
-            gr.Markdown("This module is used to extract details filled by a bank customer from cheques. At present the model is trained to extract details like - payee_name, amount_in_words, amount_in_figures. \
-            This model can be further trained to parse additional details like micr_code, cheque_number, account_number, etc")
+            gr.Markdown("This module is used to extract details filled by a bank customer from cheques. At present the model is trained to extract details like - Payee Name, Amount in words, Amount in Figures, Bank Name and Cheque Date.  \
+            This model can be further trained to parse additional details like MICR Code, Cheque Number, Account Number, etc.   \
+            Additionally, the app compares if the extracted legal & courtesy amount are matching which is an important check done during approval process of cheques. \
+            It also checks if the cheque is stale. A cheque is considered stale if it is presented to the bank 3 months after the date mentioned on the cheque.")
             with gr.Box():
                 gr.Markdown("**Upload Cheque**")
                 input_image_parse = gr.Image(type='filepath', label="Input Cheque")
@@ -34,6 +37,7 @@ with demo:
                 amt_in_words = gr.Textbox(label="Courtesy Amount")
                 amt_in_figures = gr.Textbox(label="Legal Amount")
                 cheque_date = gr.Textbox(label="Cheque Date")
+                bank_name = gr.Textbox(label="Bank Name")
                 
                 amts_matching = gr.Checkbox(label="Legal & Courtesy Amount Matching")
                 stale_check = gr.Checkbox(label="Stale Cheque")
@@ -48,8 +52,8 @@ with demo:
                             [payee_name,amt_in_words,amt_in_figures,cheque_date],parse_cheque_with_donut,cache_examples=False)
         
     
-    parse_cheque.click(parse_cheque_with_donut, inputs=input_image_parse, outputs=[payee_name,amt_in_words,amt_in_figures,cheque_date,amts_matching,stale_check])
+    parse_cheque.click(parse_cheque_with_donut, inputs=input_image_parse, outputs=[payee_name,amt_in_words,amt_in_figures,bank_name,cheque_date,amts_matching,stale_check])
                                     
-    gr.Markdown('\n Solution built by: <a href=\"https://www.linkedin.com/in/shivalika-singh/\">Shivalika Singh</a>')
+    gr.Markdown('\n Solution built by: <a href=\"https://twitter.com/singhshiviii/\">Shivalika Singh</a>')
     
-demo.launch()
+demo.launch()