The Image Content Retrieval API allows you to convert images containing text into structured data that can be digitally processed and analyzed. Its main function is to automatically identify words, lines, and blocks of text within an image, providing not only the textual content, but also information about the exact location of each element within the image using bounding box coordinates. This facilitates tasks such as extracting data from documents, passports, invoices, forms, or any image containing text.
Each word recognized by the API includes a confidence value that indicates the probability that the recognition is correct, allowing you to filter or review the results based on their accuracy. The API organizes information hierarchically: texts are grouped into blocks, blocks contain paragraphs, and paragraphs contain lines and individual words. This structure makes it easy to analyze complex documents and maintain the context of the extracted text.
In addition to textual transcription, the API can capture formatting information such as punctuation, capitalization, and word separations, and can provide metadata useful for document processing, search, and automated analysis applications. The output includes normalized coordinates (values between 0 and 1) representing the position of the text in the image, enabling visual reconstruction of the content or integration with marking and annotation systems.
The API is particularly useful in scenarios where physical or scanned documents need to be digitized, data entry processes need to be automated, or document reading systems need to be built for auditing, identity control, or document management. Its modular and detailed approach allows for both rapid text extraction and deeper analysis, including the validation of sensitive data such as names, identification numbers, and dates, as seen in an example of Haitian passport recognition, where names, dates, and codes are extracted in a hierarchical and detailed manner.
In summary, this API combines optical character recognition, precision in the location of each word, and hierarchical structure to convert images into reliable and actionable textual data.
Convierte imágenes en texto detecta palabras líneas y bloques proporcionando coordenadas confianza y estructura jerárquica del contenido visual
Extracción de texto - Características del Endpoint
| Objeto | Descripción |
|---|---|
image_url |
[Requerido] Enter a image URL |
{"status":true,"text":": AYITI: am PASPO re Py asia Etat oa ta PASSEPORT Aalto! Type ne f : BEB aon HTL 920000018 CHERUBIN Nea! Priam Kote 1 it! Lieu ce reetcceras NIRKA PORT~AU-PRINCE Moun bs reve‘ Rabongate HAITIENNE Oat H M41! Date ds calecance 6 MAI 1962 Fi catyer green! Seer FEMININ Gat peeps » set Dots Senissinn SIVAT) MET PASPO 4 17 DECEMBRE 1992 MSIGNGTURE Ou TITULAIRE amt parno 3 tn Dots Pespirotca 16 DECEMBRE 1997 920000018 SPECIMEN","boxCoordinates":[0.1197110423116615,0.05071315372424723,0.8421052631578947,0.8557844690966719],"blocks":[{"paragraphs":[{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]}],"boxCoordinates":[0,0,0,0]}],"boxCoordinates":[0,0,0,0]},{"paragraphs":[{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]}],"boxCoordinates":[0,0,0,0]},{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]},{"text":": AYITI: am","words":[{"text":":","boxCoordinates":[0.20227038183694532,0.08240887480190175,0.0030959752321981426,0.003169572107765452],"confidence":0.0},{"text":"AYITI:","boxCoordinates":[0.6945304437564499,0.05071315372424723,0.0608875128998968,0.06497622820919176],"confidence":0.0},{"text":"am","boxCoordinates":[0.762641898864809,0.06656101426307448,0.048503611971104234,0.07131537242472266],"confidence":46.0}],"boxCoordinates":[0.20227038183694532,0.05071315372424723,0.608875128998968,0.08716323296354993]},{"text":"PASPO re Py asia Etat","words":[{"text":"PASPO","boxCoordinates":[0.15067079463364294,0.08557844690966719,0.06398348813209494,0.05705229793977813],"confidence":87.0},{"text":"re","boxCoordinates":[0.5479876160990712,0.11727416798732171,0.022703818369453045,0.017432646592709985],"confidence":21.0},{"text":"Py","boxCoordinates":[0.5851393188854489,0.12519809825673534,0.009287925696594427,0.030110935023771792],"confidence":46.0},{"text":"asia","boxCoordinates":[0.608875128998968,0.10935023771790808,0.04953560371517028,0.05229793977812995],"confidence":32.0},{"text":"Etat","boxCoordinates":[0.6656346749226006,0.10935023771790808,0.034055727554179564,0.039619651347068144],"confidence":13.0}],"boxCoordinates":[0.15067079463364294,0.08557844690966719,0.5490196078431373,0.07606973058637084]},{"text":"oa ta","words":[{"text":"oa","boxCoordinates":[0.5954592363261094,0.11410459587955626,0.02476780185758514,0.06497622820919176],"confidence":25.0},{"text":"ta","boxCoordinates":[0.6336429308565531,0.14580031695721077,0.02476780185758514,0.01901743264659271],"confidence":29.0}],"boxCoordinates":[0.5954592363261094,0.11410459587955626,0.0629514963880289,0.06497622820919176]}],"boxCoordinates":[0.15067079463364294,0.05071315372424723,0.6604747162022704,0.12836767036450078]}],"boxCoordinates":[0.15067079463364294,0.05071315372424723,0.6604747162022704,0.12836767036450078]},{"paragraphs":[{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]}],"boxCoordinates":[0,0,0,0]},{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]},{"text":"PASSEPORT Aalto! Type ne f :","words":[{"text":"PASSEPORT","boxCoordinates":[0.1197110423116615,0.12678288431061807,0.11661506707946337,0.05229793977812995],"confidence":91.0},{"text":"Aalto!","boxCoordinates":[0.3632610939112487,0.14580031695721077,0.04231166150670795,0.01901743264659271],"confidence":0.0},{"text":"Type","boxCoordinates":[0.41382868937048506,0.14580031695721077,0.03199174406604747,0.022187004754358162],"confidence":88.0},{"text":"ne","boxCoordinates":[0.5108359133126935,0.11568938193343899,0.029927760577915376,0.0491283676703645],"confidence":47.0},{"text":"f","boxCoordinates":[0.5500515995872033,0.14580031695721077,0.0030959752321981426,0.01901743264659271],"confidence":18.0},{"text":":","boxCoordinates":[0.8224974200206399,0.1648177496038035,0.0030959752321981426,0.003169572107765452],"confidence":10.0}],"boxCoordinates":[0.1197110423116615,0.11568938193343899,0.7058823529411765,0.06339144215530904]},{"text":"BEB aon HTL 920000018","words":[{"text":"BEB","boxCoordinates":[0.3622291021671827,0.1901743264659271,0.04231166150670795,0.05388272583201268],"confidence":54.0},{"text":"aon","boxCoordinates":[0.4169246646026832,0.1838351822503962,0.02786377708978328,0.08399366085578447],"confidence":27.0},{"text":"HTL","boxCoordinates":[0.5696594427244582,0.19334389857369255,0.04437564499484004,0.03169572107765452],"confidence":40.0},{"text":"920000018","boxCoordinates":[0.7275541795665634,0.19175911251980982,0.14138286893704852,0.03645007923930269],"confidence":95.0}],"boxCoordinates":[0.3622291021671827,0.1838351822503962,0.5067079463364293,0.08399366085578447]}],"boxCoordinates":[0.1197110423116615,0.11568938193343899,0.7492260061919505,0.15213946117274169]}],"boxCoordinates":[0.1197110423116615,0.11568938193343899,0.7492260061919505,0.15213946117274169]},{"paragraphs":[{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]}],"boxCoordinates":[0,0,0,0]},{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]},{"text":"CHERUBIN","words":[{"text":"CHERUBIN","boxCoordinates":[0.3622291021671827,0.24722662440570523,0.1259029927760578,0.03486529318541997],"confidence":90.0}],"boxCoordinates":[0.3622291021671827,0.24722662440570523,0.1259029927760578,0.03486529318541997]}],"boxCoordinates":[0.3622291021671827,0.24722662440570523,0.1259029927760578,0.03486529318541997]},{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]},{"text":"Nea! Priam Kote 1 it! Lieu ce reetcceras","words":[{"text":"Nea!","boxCoordinates":[0.36119711042311664,0.29635499207606975,0.03508771929824561,0.022187004754358162],"confidence":32.0},{"text":"Priam","boxCoordinates":[0.40350877192982454,0.29160063391442154,0.048503611971104234,0.02377179080824089],"confidence":28.0},{"text":"Kote","boxCoordinates":[0.7481940144478845,0.29952456418383516,0.043343653250773995,0.01901743264659271],"confidence":60.0},{"text":"1","boxCoordinates":[0.7884416924664602,0.2884310618066561,0.006191950464396285,0.04595879556259905],"confidence":39.0},{"text":"it!","boxCoordinates":[0.803921568627451,0.29952456418383516,0.02063983488132095,0.01901743264659271],"confidence":0.0},{"text":"Lieu","boxCoordinates":[0.8297213622291022,0.3011093502377179,0.026831785345717233,0.017432646592709985],"confidence":0.0},{"text":"ce","boxCoordinates":[0.8637770897832817,0.3058637083993661,0.015479876160990712,0.012678288431061807],"confidence":48.0},{"text":"reetcceras","boxCoordinates":[0.8885448916408669,0.3074484944532488,0.06604747162022703,0.011093502377179081],"confidence":0.0}],"boxCoordinates":[0.36119711042311664,0.2884310618066561,0.5933952528379773,0.04595879556259905]},{"text":"NIRKA PORT~AU-PRINCE","words":[{"text":"NIRKA","boxCoordinates":[0.36119711042311664,0.3248811410459588,0.07946336429308566,0.03169572107765452],"confidence":91.0},{"text":"PORT~AU-PRINCE","boxCoordinates":[0.7430340557275542,0.329635499207607,0.21878224974200206,0.03486529318541997],"confidence":0.0}],"boxCoordinates":[0.36119711042311664,0.3248811410459588,0.6006191950464397,0.039619651347068144]},{"text":"Moun bs reve‘ Rabongate","words":[{"text":"Moun","boxCoordinates":[0.36119711042311664,0.37083993660855785,0.038183694530443756,0.020602218700475437],"confidence":68.0},{"text":"bs","boxCoordinates":[0.4086687306501548,0.37400950871632327,0.010319917440660475,0.01584786053882726],"confidence":53.0},{"text":"reve‘","boxCoordinates":[0.4262125902992776,0.36450079239302696,0.038183694530443756,0.04120443740095087],"confidence":17.0},{"text":"Rabongate","boxCoordinates":[0.46852425180598556,0.36450079239302696,0.07223942208462332,0.04120443740095087],"confidence":0.0}],"boxCoordinates":[0.36119711042311664,0.36450079239302696,0.17956656346749225,0.04120443740095087]}],"boxCoordinates":[0.36119711042311664,0.2884310618066561,0.6006191950464397,0.11727416798732171]},{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]},{"text":"HAITIENNE","words":[{"text":"HAITIENNE","boxCoordinates":[0.36119711042311664,0.40253565768621236,0.14138286893704852,0.03486529318541997],"confidence":91.0}],"boxCoordinates":[0.36119711042311664,0.40253565768621236,0.14138286893704852,0.03486529318541997]}],"boxCoordinates":[0.36119711042311664,0.40253565768621236,0.14138286893704852,0.03486529318541997]},{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]},{"text":"Oat H M41! Date ds calecance","words":[{"text":"Oat","boxCoordinates":[0.36119711042311664,0.4469096671949287,0.023735810113519093,0.017432646592709985],"confidence":72.0},{"text":"H","boxCoordinates":[0.3931888544891641,0.4500792393026941,0.008255933952528379,0.014263074484944533],"confidence":0.0},{"text":"M41!","boxCoordinates":[0.40970072239422084,0.44532488114104596,0.02476780185758514,0.020602218700475437],"confidence":0.0},{"text":"Date","boxCoordinates":[0.43962848297213625,0.4500792393026941,0.033023735810113516,0.017432646592709985],"confidence":70.0},{"text":"ds","boxCoordinates":[0.478844169246646,0.44849445324881143,0.015479876160990712,0.020602218700475437],"confidence":68.0},{"text":"calecance","boxCoordinates":[0.5025799793601651,0.4532488114104596,0.06501547987616099,0.01584786053882726],"confidence":0.0}],"boxCoordinates":[0.36119711042311664,0.44532488114104596,0.20639834881320948,0.02377179080824089]}],"boxCoordinates":[0.36119711042311664,0.44532488114104596,0.20639834881320948,0.02377179080824089]},{"lines":[{"text":"","words":[],"boxCoordinates":[0,0,0,0]},{"text":"6 MAI 1962","words":[{"text":"6","boxCoordinates":[0.36119711042311664,0.48019017432646594,0.015479876160990712,0.03328050713153724],"confidence":94.0},{"text":"MAI","boxCoordinates":[0.3931888544891641,0.48019017432646594,0.04540763673890609,0.03328050713153724],"confidence":94.0},{"text":"1962","boxCoordinates":[0.45717234262125905,0.4786053882725832,0.06191950464396285,0.03645007923930269],"confidence":95.0}],"boxCoordinates":[0.36119711042311664,0.4786053882725832,0.15789473684210525,0.03645007923930269]}],"boxCoordinates":[0.36119711042311664,0.4786053882725832,0.15789473684210525,0.03645007923930269]}]}],"_note":"Response truncated for documentation purposes"}
curl --location --request POST 'https://zylalabs.com/api/11264/image+content+retrieval+api/21264/text+extraction?image_url=https://static-content.regulaforensics.com/Hardware-products/knowledge_hub/glossary_documents/PASSPORT/2l.webp' --header 'Authorization: Bearer YOUR_API_KEY'
| Encabezado | Descripción |
|---|---|
Autorización
|
[Requerido] Debería ser Bearer access_key. Consulta "Tu Clave de Acceso a la API" arriba cuando estés suscrito. |
Sin compromiso a largo plazo. Mejora, reduce o cancela en cualquier momento. La Prueba Gratuita incluye hasta 50 solicitudes.
El extremo de extracción de texto devuelve datos estructurados que incluyen texto reconocido coordenadas de cuadros delimitadores para cada palabra línea y bloque puntuaciones de confianza que indican la precisión del reconocimiento y una organización jerárquica del texto bloques párrafos líneas palabras
Los campos clave en los datos de respuesta incluyen "texto" (el contenido reconocido) "coordenadas" (posiciones del cuadro delimitador) "confianza" (puntuación de precisión) y "jerarquía" (estructura que indica bloques párrafos líneas y palabras)
Los datos de respuesta están organizados jerárquicamente con bloques que contienen párrafos párrafos que contienen líneas y líneas que contienen palabras individuales Esta estructura permite una fácil navegación y análisis del texto extraído
El punto final proporciona información como texto reconocido su ubicación dentro de la imagen niveles de confianza para cada reconocimiento y detalles de formato como puntuación y mayúsculas lo que lo hace adecuado para varios tipos de documentos
Los usuarios pueden personalizar sus solicitudes especificando parámetros como formato de imagen configuraciones de idioma y estructura de salida deseada lo que permite una extracción adaptada según tipos de documentos o requisitos específicos
La precisión de los datos se mantiene a través de avanzados algoritmos de reconocimiento óptico de caracteres que incluyen puntuaciones de confianza para cada elemento reconocido, lo que permite a los usuarios filtrar resultados en función de su fiabilidad
Los casos de uso típicos incluyen la digitalización de documentos escaneados la automatización de la entrada de datos de formularios o facturas y la construcción de sistemas de lectura de documentos para la verificación de identidad o propósitos de auditoría
Los usuarios deben verificar los puntajes de confianza en la respuesta puntajes bajos pueden indicar resultados parciales o inexactos Implementar un proceso de revisión para entradas de baja confianza puede ayudar a asegurar la calidad y la completitud de los datos
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