This document discusses using optical character recognition (OCR) software for AskMom's receipt digitization services. [1] OCR accuracy depends on input quality and is typically lower for receipts due to variations in fonts, spacing, and quality, requiring human review. [2] While OCR could handle initial data entry, it cannot replace humans due to limitations in language models and an inability to reliably correct mistakes without human input. [3] AskMom's business model prioritizes human interaction and flexibility over full automation in order to offer accurate receipt digitization globally.

1. AN UPDATE
Prepared by Nadia Millington & Luis Rosenthal

2. Quality of phone
• Ideally Nokia 6300 ( or above) will
allow appropriate visualisation of the
image is its resolution and screen size.
• If microworkers do not have an
appropriate phone, they can access
this phone via a microfinance loan or
we can develop a scheme whereby
refurbished high end phones from the
first world ( which have been fully
depreciated) can be sent to the BOP
at a fraction of the cost ( some as low
as 20USDs) allowing for high
visualisation and good quality screen
size.

3. Data transmission costs
The money that the
microworkers earn is expected to
be significantly higher than the
data costs based on our quick
and dirty review of phone costs
in 3 developing countries.
Assuming each job pays 20US
cents we see data charges as a
small percentage of their
earnings and their only cost
(2-15%). We expect even these
percentages to be reduced based
on a thorough review of all the
available packages

4. Can the services be automated by a computer?
High accuracy OCR software can read more than 400
The accuracy of OCR systems is, in practice, directly
characters/second.
dependent upon the quality of the input documents.
OCR is not very tolerant of bad picture quality unlike
However: human readers. As such it is expected the OCR use
OCR software is not efficient in recognizing handwriting and with receipt will have higher error thresholds. The
distinguishing between fonts which are quite similar to main difficulties encountered with receipts , invoices
handwriting. In such cases manual entry plays better role etc that are a challenge to OCR are
than OCR process.
Data entry provides complete flexibility allowing micro • Variations in shape, due to serifs and style
operators to prepare digital documents from multiple variations.
formats- even audio recording of spending can be included, • Deformations, caused by broken characters,
and notes on partial payments scribbled on the receipts smudged characters and speckle.
etc. • Variations in spacing, due to subscripts,
superscripts, skew and variable spacing.
OCR may be efficient during the initial level of data entry
• Mixture of text and graphics.
service but cannot be a substitute of data entry service
because recognition of typewritten text is still not 100%
accurate even where clear imaging is available. OCR
software ranges from 71% to 95%; but total accuracy can
be achieved only by human review. Errors occur because
of :
•Distinguishing noise from text- Dots and accents may be
mistaken for noise, and vice versa.
•Mistaking graphics or geometry for text- This leads to
nontext being sent to recognition.
ni = m
• Mistaking text for graphics or geometry- In this case the
text will not be passed to the recognition stage. This often Common OCR issues include mistaking an “ni” for an “m”
happens if characters are connected to graphics.

5. When OCR doesn’t work
These imperfections may affect and cause problems in different parts of the recognition process of an
OCR-system, resulting misclassifications

6. Finally
Most OCR has some human interaction. Modern optical character
recognition software relies on human interaction to correct
misrecognized characters. Even though the software often reliably
identifies low-confidence output, the simple language and
vocabulary models employed are insufficient to automatically
correct mistakes. A developer of the software lemon.com confirms
this- he states “Whenever the machine learning system or the OCR
system have a low confidence result, it can ask for human
assistance, usually with a multiple choice answer or a request to
edit an entry”.
Models where OCR does not use human intervention, the
consumer is expected to correct their own errors which is not a
value proposition AskMom would ever employ as we are selling
convenience
It is possible to enhance the AskMom Business model with OCR
technology on the front end utilising microworkers for quality
assurance and low confidence results. The use of micro workers
would still mean that we are operating at costs below other
players. However, the human element is the key as it differentiates
us. It allows AskMom to have higher levels of flexibility for
recording complex, ill printed, receipts with accuracy from all parts
of the world (offering a global solution) as opposed to the other
options like lemon which only works within the US jurisdiction