Abstract
Bank cheques (checks) are still widely used all over the world for financial transactions. Huge volumes of handwritten bank cheques are processed manually every day in developing countries. In such a manual verification, user written information including date, signature, legal and courtesy amounts present on each cheque has to be visually verified. As many countries use cheque truncation systems (CTS) nowadays, much time, effort and money can be saved if this entire process of recognition, verification and data entry is done automatically using images of cheques. An attempt is made in this paper to present the state of the art in automatic processing of handwritten cheque images. It discusses the important results reported so far in preprocessing, extraction, recognition and verification of handwritten fields on bank cheques and highlights the positive directions of research till date. The paper has a comprehensive bibliography of many references as a support for researchers working in the field of automatic bank cheque processing. The paper also contains some information about the products available in the market for automatic cheque processing. To the best of our knowledge, there is no survey in the area of automatic cheque processing, and there is a need of such a survey to know the state of the art.
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Abbreviations
- ANN:
-
Artificial neural network
- BC:
-
Bayesian classifier
- BN:
-
Bayesian network
- BPNN:
-
Back-propagation neural networks
- CM:
-
Co-occurrence matrix
- CTS:
-
Cheque truncation system
- DBC:
-
Differential box counting
- DFA:
-
Deterministic finite automation
- DTW:
-
Dynamic time warping
- ED:
-
Euclidean distance
- EDF:
-
Extended drop fall
- EER:
-
Equal error rate
- FAR:
-
False acceptance rate
- FFNN:
-
Feed-forward neural network
- FKNN:
-
Fuzzy K-nearest neighbour
- FNN:
-
Fuzzy neural network
- FPS:
-
Fixed point-spread
- FRR:
-
False rejection rate
- GB:
-
Global baseline
- GLS:
-
Grey-level space
- GRNN:
-
Generalized regression neural network
- GSC:
-
Gradient, structural and concavity
- HDF:
-
Hybrid drop fall
- HDS:
-
Hit and deflect strategy
- HMM:
-
Hidden Markov models
- HMRF:
-
Hidden Markov random field
- HNN:
-
Hopfield neural nets
- HNNC:
-
Hierarchical neural network classifier
- HT:
-
Hough transform
- ICS:
-
Image-based clearing system
- IQA:
-
Image quality assurance
- IRD:
-
Image replacement document
- KNN:
-
K-nearest neighbour
- LBP:
-
Local binary pattern
- LGSD:
-
Local granulometric size distributions
- LS-SVM:
-
Least squares support vector machines
- MBR:
-
Minimum bounding rectangle
- MD:
-
Mahalanobis distance
- MDC:
-
Minimum distance classifier
- ME:
-
Multi expert
- MICR:
-
Magnetic ink character recognition
- ML:
-
Maximum likelihood
- MLP:
-
multi-layer perceptron
- MQDF:
-
Modified quadratic discriminant function
- MM:
-
Mathematical morphology
- MMI:
-
Maximum mutual information
- MPR:
-
Most probable region
- MRS:
-
Multi resolution shape
- MSFC:
-
Multiple structural feature classifier
- MSI:
-
Model Sub-Image
- MVBC:
-
Majority vote method based on Borda count function
- NN:
-
Neural network
- NNC:
-
Nearest neighbour classifier
- OCR:
-
Optical character recognition
- OGMM:
-
Orthogonal Gaussian mixture model
- PCAC:
-
Principal component analysis classifier
- PCC:
-
Pseudo-cepstral coefficients
- PF:
-
Pressure features
- PGM:
-
Probabilistic graphical model
- PNV:
-
Payee Name Verification
- RBF:
-
Radial basis function
- RBFNN:
-
Radial basis function neural network
- ROC:
-
Receiver operating characteristic
- RPBF:
-
Reference pattern based features
- RS:
-
Random subspaces
- SB:
-
Structural-based
- SC:
-
Symbolic classifier
- SDT:
-
Syntax directed translation
- SF:
-
Slant features
- SLFFNN:
-
Simple-layer feed-forward neural network
- SOM:
-
Self-organizing map
- SSE:
-
Sum-of-squared error
- SVM:
-
Support vector machines
- TB:
-
Template based
- TS:
-
Takagi–Sugeno
- TDNN:
-
Time delay neural network
- TSI:
-
Target sub image
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Jayadevan, R., Kolhe, S.R., Patil, P.M. et al. Automatic processing of handwritten bank cheque images: a survey. IJDAR 15, 267–296 (2012). https://doi.org/10.1007/s10032-011-0170-8
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DOI: https://doi.org/10.1007/s10032-011-0170-8