This document discusses developing an automated system for processing handwritten Indian bank cheques. It outlines some of the key challenges with manual cheque processing including human errors, expenses, and delays. The proposed solution is to automate cheque processing using computer vision and deep learning techniques. This includes extracting regions of interest from scanned cheque images, segmenting handwritten digits, identifying digits using neural networks, and integrating the system as a web service for digitization and auditing of cheque transactions. The system achieves overall accuracy of 95-98% but continues to face challenges with issues like joined digits, image quality, and decimal values.

1. Automated Cheque Processing of
Indian Bank Cheques

2. Story of Cheque Transactions
Bengal Bank,
established in 1784,
was the first bank to
introduce cheque
system in India.
Majority the
cheques processed
are hand written
In 2017, 405 million
cheques were used
for payments and to
acquire cash across
the UK

3. The Problem Begins…
Manual evaluation
Human errors
Workforce
Expensive

4. Proposed Solution
Automation
Recognition of handwritten
information (digits)
Reduce manual effort
Reduce delays
Validations

5. What More?
Webservice Integration
Digitization for auditing
Faster operations
Parallel Processing

6. How is it done?
Automated Handwritten Digit
Recognition
Fragmentation and
Identification
Neural Networks
Validation

7. What did we use?
 MNIST Dataset
 Python
 Image processing libraries (PIL)
 Computer Vision libraries (OpenCV)
 Keras with Tensorflow Backend
 CNN

8. Preprocessing
 Extract Amount ROI
 Identifying ROI using
pattern Matching
 Convert to greyscale (invert)
and intensify
 Extract Date ROI
 Segment Date ROI and Amount ROI, to get
individual digit images
 Resize the digit images to 28 x 28

9. Algorithms
 Random Forest
 Pros:
 Used for uneven dataset
and missing values
 Cons:
 Tendency to Overfit
 Neural Networks
 Pros:
 Fault tolerance, parallel
processing
 Cons:
 Blackbox solution

10. Architecture
Scanned
Images
Image
Processor
ROI Extractor
Digit
Segmenter
Digit Identifier
using Model
Combiner
Business
Validator

11. Code Concepts
 Image Processing
 Resize
 Convert to greyscale (Invert)
 Intensify
 ROI Extraction
 Sectioning the area
 Pattern Matching

12. Code Concepts
 Digit Segmentation
 Find Contours
 Find bounding boxes and extract
 Pad digit with black background
 Digit Identification
 Load model and identify the digit

13. Code Concepts
 Combiner
 Get date string and convert to datetime
 Get Amount as string and convert to int
 Business Validation
 Read Validation CSV and Compare Values
 Print Results
amtPredicted dtPredicted fname CorrectAmtPrediction CorrectDtPrediction Lessthan90days validCheque humanValNeeded
567 29/11/18cheque_3_noisy.jpg TRUE TRUE TRUE TRUE 0
29 19/07/15
cheque_4_amountStarted0.jp
g TRUE TRUE FALSE FALSE 0
10000 11/02/19cheque_5_comma.jpg TRUE TRUE TRUE TRUE 0
1234 10/10/18cheque_2.jpg TRUE TRUE FALSE FALSE 0
127 22/05/17hdfc_joinedNumbers.jpg FALSE FALSE FALSE FALSE 1
890 01/12/18cheque_1.jpg TRUE TRUE TRUE TRUE 0

14. Code Concepts
 Model
 Image Data Generator

15. Results
 ROI Extraction accuracy
IOU / Jaccard Index
 MNIST accuracy
Model training
Overall accuracy 95-98%
Digit level misclassifications
Confusion Matrix
Classification Report

16. Production Plan
 Use more than one algorithm to predict
digits
 Provide as a web service
 Provide more configurable options for
generalization
 Use rule based predictions for Date

17. Challenges
 Joined Digits
 Model accuracy for Digits like 0 and 1
 Orientation of Image
 Image Quality and Noise
 Special symbols
 Decimal values