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updated model to extract bank_name and cheque_date
Browse files
app.py
CHANGED
@@ -12,18 +12,21 @@ demo = gr.Blocks()
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with demo:
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gr.Markdown("# **<p align='center'>ChequeEasy: Banking with Transformers </p>**")
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gr.Markdown("ChequeEasy is a project that aims to simplify the process of approval of cheques
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This project leverages Donut model proposed in
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"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
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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. \
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Donut helps you get rid of all of these OCR specific limitations. The model for the project has been trained using
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with gr.Tabs():
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with gr.TabItem("Cheque Parser"):
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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 -
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This model can be further trained to parse additional details like
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with gr.Box():
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gr.Markdown("**Upload Cheque**")
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input_image_parse = gr.Image(type='filepath', label="Input Cheque")
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@@ -34,6 +37,7 @@ with demo:
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amt_in_words = gr.Textbox(label="Courtesy Amount")
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amt_in_figures = gr.Textbox(label="Legal Amount")
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cheque_date = gr.Textbox(label="Cheque Date")
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amts_matching = gr.Checkbox(label="Legal & Courtesy Amount Matching")
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stale_check = gr.Checkbox(label="Stale Cheque")
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@@ -48,8 +52,8 @@ with demo:
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[payee_name,amt_in_words,amt_in_figures,cheque_date],parse_cheque_with_donut,cache_examples=False)
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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])
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gr.Markdown('\n Solution built by: <a href=\"https://
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demo.launch()
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with demo:
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gr.Markdown("# **<p align='center'>ChequeEasy: Banking with Transformers </p>**")
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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. \
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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." \
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"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. \
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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. \
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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. \
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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.")
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with gr.Tabs():
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with gr.TabItem("Cheque Parser"):
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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. \
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This model can be further trained to parse additional details like MICR Code, Cheque Number, Account Number, etc. \
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Additionally, the app compares if the extracted legal & courtesy amount are matching which is an important check done during approval process of cheques. \
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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.")
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with gr.Box():
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gr.Markdown("**Upload Cheque**")
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input_image_parse = gr.Image(type='filepath', label="Input Cheque")
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amt_in_words = gr.Textbox(label="Courtesy Amount")
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amt_in_figures = gr.Textbox(label="Legal Amount")
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cheque_date = gr.Textbox(label="Cheque Date")
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bank_name = gr.Textbox(label="Bank Name")
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amts_matching = gr.Checkbox(label="Legal & Courtesy Amount Matching")
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stale_check = gr.Checkbox(label="Stale Cheque")
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[payee_name,amt_in_words,amt_in_figures,cheque_date],parse_cheque_with_donut,cache_examples=False)
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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])
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gr.Markdown('\n Solution built by: <a href=\"https://twitter.com/singhshiviii/\">Shivalika Singh</a>')
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demo.launch()
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