Mudra — OCR built for the documents other engines give up on
Mudra Book a call

Stop retyping what is already on the page.

Mudra reads your scanned documents in the script they were written in and pulls out the fields your team keys in by hand.

The problem

Re-keying is the bottleneck. Every scan waits on a person.

{{ st.num }}

{{ st.title }}

{{ st.body }}

{{ st.stat }} {{ st.statNote }}
{{ st.num }}

{{ st.title }}

Scroll ↓
ONE DOCUMENT, KEYED BY HAND
SOURCE SCAN Name · M. A. Al-Sharif Born · 14 / 03 / 1987 Issued · Cairo Registry · 442918
→
YOUR SYSTEMKEYING…
M. A. Al-Sharif
1987-03-1
WHERE ONE WRONG FIELD ENDS UP
INTAKE 1987-14-03 LOOKS VALID
TRANSLATE passes through
FILING rejected · date invalid CAUGHT HERE
REWORK 1987-03-14 +$53
CAPACITY MONITOR — pages/wk one hire can process
CAPACITY · 500/WK
710 PAGES/WK RECEIVED 500/WK CAPACITY · 42% OVER
HANDLED UNSTAFFED, BACKLOG GROWS
BACKLOG, MON–FRI
62
99
133
171
210

English is the easy case.

Off-the-shelf OCR was built for clean, printed English, and holds up there. Everywhere else, the output still looks plausible — a wrong field that reads like a right one goes straight through.

English handwriting ~99% of characters read correctly on a page of English handwriting. That case is close to solved.
Every other script 1 in 10 sentences or fewer come back correct in most of the world's other writing systems, on every model tested.
The solution

Mudra reads the page and hands back the record.

The scan goes in as it arrives. Every field comes back with a confidence score, and anything the model is unsure of is flagged for a person instead of guessed.

{{ doc.kind }} READING
{{ doc.title }}
MATCH
{{ l.native }}
SEAL
1987
{{ doc.query }} {{ doc.hits }}
Extracted record TRANSCRIBE TRANSLATE
{{ r.label }}
{{ r.value }} {{ r.value }} {{ r.conf }} {{ r.conf }} · REVIEW
{{ progress }} LOW-CONFIDENCE VALUES ARE FLAGGED, NEVER GUESSED
How it works

Three steps, start to finish.

Get a demo →
{{ hw.num }} {{ hw.label }}

{{ hw.title }}

{{ hw.body }}

PDF · JPG · TIFF
DROP PAGES
RECEIVED
scan_0041.tif 2.1 MB ✓
birth_cert_ar.pdf 840 KB ✓
invoice_1875.jpg 1.4 MB ✓
+ 17 MORE
SAMPLE PAGES NO PREP NEEDED
TRANSCRIBE TRANSLATE
P.1
P.2
P.3
P.4
READ BACK FROM شهادة ميلاد ARABIC · 300 DPI
DATE OF BIRTH {{ xv1 }}
0.97
PLACE OF REGISTRATION {{ xv2 }}
0.64
REGISTRY NO. {{ xv3 }}
0.99
3 OF 4 ABOVE 0.95
VALUECONF.
{{ feedRows }}
CSV JSON API 2 FLAGGED FOR REVIEW
Who it's for

Built for the desks doing the keying.

We transcribe, translate, and make foreign-language documents searchable, for the teams that handle them every day.

{{ w.role }} {{ w.queue }}
شهادة ميلاد الاسم: أميرة حداد اسم الأب: سامي حداد اسم الأم: ليلى منصور تاريخ الميلاد: ١٧/٠٤/١٩٩١ مكان الإصدار: عمّان
TRANSCRIPT TRANSCRIBETRANSCRIBETRANSLATETRANSLATE
NAME{{ w.v1 }}
FATHER{{ w.v2 }}
MOTHER{{ w.v3 }}
BORN{{ w.v4 }}
ISSUED{{ w.v5 }}
FIELDS 5 / 5
بطاقة هوية I-130 · EXH. B
الاسم: أميرة حداد الجنسية: الأردن رقم الوثيقة: P4 892 3310 تاريخ الميلاد: ١٧/٠٤/١٩٩١ مكان الإصدار: عمّان
P<JORHADDAD<<AMIRA<<<<<<<<<<
P4892331045JOR9104178M3108024
TRANSCRIPTTRANSCRIBETRANSCRIBETRANSLATETRANSLATE
FULL NAME{{ w.v1 }}
NATIONALITY{{ w.v2 }}
DOCUMENT NO.{{ w.v3 }}
EXPIRY{{ w.v4 }}
发票 / 装箱单 · NO. 2026-0418 CNTR MSKU 482 9130
品名 · HS数量{{ w.mHead }}
阀门 8481.80 12 → {{ w.m1 }}
管接头 7307.19 08 → {{ w.m2 }}
垫片 4016.93 14 → {{ w.m3 }}
螺栓 7318.15 08 → {{ w.m4 }}
SHIPPER {{ w.v1 }} DECLARED {{ w.v4 }}
4 LINES MAPPED TRANSCRIBETRANSCRIBETRANSLATETRANSLATE
{{ w.p1t }} CL-11872
{{ w.pL1 }}{{ w.v1 }}
{{ w.pL2 }}09.03.2026
{{ w.pL3 }}4 120,00
{{ w.p1n }}
{{ w.p2t }} CL-11873
{{ w.pL1 }}{{ w.v2 }}
{{ w.pL2 }}09.03.2026
{{ w.pL3 }}1 860,00
{{ w.p2n }}
{{ w.p3t }} CL-11874
{{ w.pL1 }}{{ w.v3 }}
{{ w.pL2 }}10.03.2026
{{ w.pL3 }}7 240,00
{{ w.p3n }}
{{ w.p4t }} CL-11875
{{ w.pL1 }}{{ w.v4 }}
{{ w.pL2 }}10.03.2026
{{ w.pL3 }}2 480,00
{{ w.p4n }}
{{ w.p5t }} CL-11876
{{ w.pL1 }}{{ w.v5 }}
{{ w.pL2 }}11.03.2026
{{ w.pL3 }}9 015,00
{{ w.p5n }}
{{ w.scanLine }} TRANSCRIBETRANSCRIBETRANSLATETRANSLATE
REFCLAIMANTFILEDAMOUNT
CL-11872{{ w.v1 }}09.03.264 120
CL-11873{{ w.v2 }}09.03.261 860
CL-11874{{ w.v3 }}10.03.267 240
CL-11875{{ w.v4 }}10.03.262 480
CL-11876{{ w.v5 }}11.03.269 015
KEYED INTO SYSTEM OF RECORD

{{ w.title }}

{{ w.line }}

← Scroll →
Resources

Backed by research on document extraction.

Open benchmarks, standards and technical references behind multilingual document analysis.

اрصیﻻجیбপবกहកتᜌटصगکᓀனᐊ中ñمюअה文تবกहកتᜌटصगکᓀனᐊ中ñمюअה文تاрصیﻻجیбপکᓀனᐊ中ñمюअה文تاрصیﻻجیбপবกहកتᜌटصगה文تاрصیﻻجیбপবกहកتᜌटصगکᓀனᐊ中ñمюअیбপবกहកتᜌटصगکᓀனᐊ中ñمюअה文تاрصیﻻجटصगکᓀனᐊ中ñمюअה文تاрصیﻻجیбপবกहកتᜌ
Unicode Consortium

Every script a document can be written in.

Ligatures, diacritics, right-to-left runs, mixed-direction lines.

Unicode.org →
ICDAR · Benchmark

The Robust Reading Competition.

The academic benchmark where OCR claims get scored against shared datasets.

SAMPLE 01FIELD SCORE
Name✓
Date of issue✓
Issuing office!
rrc.cvc.uab.es →
FADGI · Standard

What makes a scan hard to read.

digitizationguidelines.gov →
WCO · Data model

The fields customs paperwork has to carry.

A shared vocabulary for cross-border declarations
wcoomd.org →
ICDAR · Challenge

Scanned receipt OCR and information extraction.

SROIE dataset →
Get started

Ten minutes, and your worst documents.

If your team is still retyping what is already on the page, that is the whole conversation. Tell us what documents you process and which fields your team keys in by hand. If it looks like a fit, we will ask for a handful of real pages, bad scans included, and show you what comes off them.

Get a demo → or contact us
← Scroll →