Szabo Roland Teodor

UBB

Obtaining personal Finance data from receipts using machine learning

Contents


  • Motivation and Problem Statement
  • Related work 
  • ReceiptBudget - the application
  • Conclusions

The results obtained in this paper were also presented at the Scientific Communication Session for Students

Motivation


  • came to college in 2011
  • money seemed to evaporate
  • needed a tool to keep track of it
  • existing ones weren't good enough

Aims



  • create a new personal finance management tool
  • develop an OCR engine tailored for receipts
  • show interactive visualizations of expenses

Optical character recognition


Steps performed

  • document layout analysis
  • character segmentation
  • character recognition

    • Existing OCR ENGINEs

    OCROpus
    • poorer recognition performance
    • does sophisticated document layout analysis
    Tesseract
    • good general performance
    • no document layout analysis

    Our OCR engine



    • knows layout of receipts
    • specially trained for receipt font
      • which is weird, compressed, broken

    Dataset

    • 20 receipts were annotated by hand
    • ~7000 characters
    • 74 classes (different characters)

    BUILT USING:



    All three free, open-source libraries for scientific computing, machine learning and computer vision

    Results

    Character Recognition


    • SVM baseline with RBF kernel - 91.01%
    • deep learning with neural networks - 98.5%
    Model Mean accuracy Std deviation
    SVM 91.01% 0.126
    SAU 95.625% 0.387
    MLP2 97.988% 1.244
    MLP3 97.576% 1.768
    MLP4 98.506% 0.256
    Most common missclassifications
    • , and . - 40 times
    • O and 0 - 39 times
    • 1, l and I - 14 times


     Character segmentation
    • Random forests
    • tried between 150 and 250 trees 
    • number of features used  between 8 and 120
    • best F1 score of 87.9%
    Confusion Matrix 

     

    No split

    Split

    Predicted no split

    4556

    363

    Predicted split

    255

    2546

    Model is more specific, rather than sensitive

    Line Classification


    • mostly regular expressions to match various patterns
    • 92.8% accuracy
    • depends a lot on results from previous step:
      • T0TAL 
      • STA REPUBLICII

    Comparison to Other Results

    • recognition results obtained by others on MNIST are better
      • SVM with RBF kernel - 98.6% accuracy
      • state-of-the-art - 99.7%
      • 10x bigger dataset 
      • 7x fewer classes
    • segmentation results for license plate are better
      • 96% accuracy
      • fixed number of letters to be segmented
    Tesseract
    ReceiptBudget


    S.C. HRTIHH 5.9.
ELUJ NHPUCH. STR. BUCEGI. NR. 19

9.9.1. 99 11735629
RUN
1.999 x 3.19
BRTISTE N9Z.CLHS|C3S 3.19 9
1.999 x 2.59
STICKLETTI CHRTUF 99 2.59 9
9.399 x 7.99
HHNDHRINE 3.12 H
9.446 x 7.99
99511 3.56 9
1.999 x 11.29
SHLHH C959 USCHT 299 11.29 9
SUBTUTHL 23.5?
SUBTUTRL _____ ‘-29:99
TUTHL 23.57
    S.C.   ARTIMA  S.A.
CLUJMAPOCA, STR. BUCEGI, NR.19
C  .U.1,R O1   1735628
RON
1.000  x  3,19
BATISTE NAZ.CLASIC3S 3,19%
1,000x   2,50
STICNLETTI  CARTOF 80 2,50 A
0,390  x  7,99
MANDARINE   3,12A
0,446  x  7,99
R0SI 3,56  A
1.000  x 11,20
SALANIASAOSCAT  290  11,20A
SUBTOTAL   23,57
SUBTOTAL 23,57
TOTAL   27,57

    The Application

    • ReceiptBudget has an interactive dashboard
    • The goal is to get some insight into spending patterns

    THE map


    The charts


    Built USING

     



    Conclusion 

    • OCR results are better than  by using off-the-shelf components
      • incorporating domain specific knowledge helps
    • interactive dashboard is helpful
      • I saved some money:)
     

    Questions?

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