A Two-stage CNN Based Computer Vision Framework for Automated Validation of Indian Bank Cheques
Pubblicato online: 20 feb 2025
Pagine: 146 - 161
DOI: https://doi.org/10.2478/ias-2024-0011
Parole chiave
© 2024 Debjani Chakraborty et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.
Figure 6.
Figure 7.
Figure 8.
Figure 9.
Figure 10.
Figure 11.
Figure 12.
Figure 13.
Quantitative Results: Detection Accuracy of Stage-Validation Module
| Model | Payee’s Name (%) | Legal Amount (%) | Courtesy Amount (%) | Signature (%) | Date (%) |
|---|---|---|---|---|---|
| Madasu [ |
90.0 | 91.6 | 85.2 | 90.3 | 87.5 |
| Hakim [ |
97.5 | 72.0 | 75.4 | 69.0 | 75.8 |
| Alirezaee [ |
94.4 | 93.2 | 94.15 | 90.8 | 92.1 |
| U-Net [ |
95.3 | 94.9 | 95.1 | 95.2 | 91.8 |
| YOLO v8 [ |
96.2 | 94.8 | 95.1 | 91.0 | 93.3 |
| Proposed Model | 98.1 | 98.0 | 97.4 | 97.2 | 98.2 |
Challenges of extracting and recognizing data fields from bank cheque images
| Challenges | Difficulties |
|---|---|
| Data deterioration | The post-binarization nose, including stamps, dots and lines are observed. The decrease of quality resulting from the removal of noise, lines, and backgrounds. |
| The problem of Skewness | Misalignment of the cheque during the scanning process. The segmentation and recognition of bank cheques pose significant challenges. |
| Distinct handwriting | The distinctive handwriting style complicates the process of data recognition. Various persons use several font sizes, directions, thicknesses, and angles. |
| Data superposition | Data overlap caused by adjacent words resulting in insufficient differentiation of the data. |
| Perplexity | “/” occasionally contacts the adjacent digits. The “/” symbol poses a challenge in the process of segmenting and recognizing numbers. |
| Document torn and folded | The corners are predominantly folded or ragged. |
| Variation in image contrast | The image is either excessively bright or excessively dark, which hinders the extraction of data. Issues with image camera calibration, subpar printing, and incorrect thresholding of the background. |
| Cheque Streaks | The presence of dark or bright streaks can be attributed to various factors, including scratches in the scan window, dirt on the camera calibration target, and malfunctions in the camera electronics. |
| Image compression | Difficulties arise when the image is compressed below the minimal requirement or when the image size exceeds the maximum range. |
| Integrated algorithm | There is currently no standardized method available that can extract all the information from a bank cheque simultaneously. |
Summary of the Dataset
| Cheques with Missing Handwritten Field | Cheques with No Missing Handwritten Field | Cheques with Overwritten/Strike-through Characters | Cheques with no Overwritten/Strike-through Characters |
|---|---|---|---|
| 90 | 30 | 78 | 12 |
Qualitative Observations of Stage-1 Validation Module’s Performances
| U-Net | YOLO v8 | Proposed Mask RCNN based model |
|---|---|---|
Qualitative Observations of Stage-2 Validation Module’s Performances
| U-Net | YOLO v8 | Proposed Mask RCNN based model |
|---|---|---|
Quantitative Results: Detection Accuracy of Stage-2 Validation Module
| Overwritten/Strikethrough class types | U-Net based model (%) | YOLO v8 based model (%) | Proposed Mask R-CNN based model (%) |
|---|---|---|---|
| Type - A | 94.30 | 95.40 | 98.20 |
| Type – B | 93.90 | 94.56 | 98.03 |
| Type – C | 94.75 | 95.2 | 97.57 |
| Type – D | 95.39 | 95.42 | 98.50 |
| Type – E | 92.67 | 93.71 | 98.90 |
| Type – F | 93.25 | 95.83 | 97.82 |
| Type – G | 95.28 | 95.07 | 98.23 |
| Type – H | 92.33 | 94.89 | 97.90 |
| Type – I | 93.80 | 94.77 | 97.88 |